Overview

Brought to you by YData

Dataset statistics

Number of variables44
Number of observations5860
Missing cells12839
Missing cells (%)5.0%
Duplicate rows11
Duplicate rows (%)0.2%
Total size in memory9.1 MiB
Average record size in memory1.6 KiB

Variable types

Numeric13
Categorical21
Text7
DateTime1
Boolean1
Unsupported1

Alerts

Category has constant value "Default" Constant
Shipping Address Country has constant value "IN" Constant
Billing Address Country has constant value "IN" Constant
Shipping Method has constant value "STD" Constant
Gift Wrap has constant value "False" Constant
HSN Code has constant value "62045300.0" Constant
GST Tax Type Code has constant value "GST12" Constant
UTGST has constant value "0" Constant
CESS has constant value "0" Constant
UTGST Rate has constant value "0" Constant
CESS Rate has constant value "0" Constant
Sale Order Status has constant value "COMPLETE" Constant
GSTIN has constant value "09AAICN9819MIZL" Constant
Dataset has 11 (0.2%) duplicate rowsDuplicates
Billing Address Id is highly overall correlated with Shipping Address IdHigh correlation
Billing Address Pincode is highly overall correlated with Billing Address State and 5 other fieldsHigh correlation
Billing Address State is highly overall correlated with Billing Address Pincode and 5 other fieldsHigh correlation
CGST is highly overall correlated with CGST Rate and 4 other fieldsHigh correlation
CGST Rate is highly overall correlated with Billing Address Pincode and 8 other fieldsHigh correlation
Channel Name is highly overall correlated with Discount and 3 other fieldsHigh correlation
Discount is highly overall correlated with Channel Name and 1 other fieldsHigh correlation
IGST is highly overall correlated with CGST and 8 other fieldsHigh correlation
IGST Rate is highly overall correlated with Billing Address Pincode and 9 other fieldsHigh correlation
Item Type Color is highly overall correlated with IGST and 3 other fieldsHigh correlation
MRP is highly overall correlated with IGST and 5 other fieldsHigh correlation
Prepaid Amount is highly overall correlated with Channel NameHigh correlation
SGST is highly overall correlated with CGST and 4 other fieldsHigh correlation
SGST Rate is highly overall correlated with Billing Address Pincode and 8 other fieldsHigh correlation
Selling Price is highly overall correlated with IGST and 4 other fieldsHigh correlation
Shipping Address Id is highly overall correlated with Billing Address IdHigh correlation
Shipping Address Pincode is highly overall correlated with Billing Address Pincode and 5 other fieldsHigh correlation
Shipping Address State is highly overall correlated with Billing Address Pincode and 5 other fieldsHigh correlation
Shipping Courier Status is highly overall correlated with Shipping Tracking StatusHigh correlation
Shipping Tracking Status is highly overall correlated with Channel Name and 1 other fieldsHigh correlation
Subtotal is highly overall correlated with Channel Name and 4 other fieldsHigh correlation
Total Price is highly overall correlated with IGST and 4 other fieldsHigh correlation
CGST Rate is highly imbalanced (60.0%) Imbalance
IGST Rate is highly imbalanced (59.7%) Imbalance
SGST Rate is highly imbalanced (60.0%) Imbalance
Shipping Courier Status is highly imbalanced (74.0%) Imbalance
Item Type Color has 4961 (84.7%) missing values Missing
MRP has 188 (3.2%) missing values Missing
Cost Price has 5860 (100.0%) missing values Missing
GST Tax Type Code has 1591 (27.2%) missing values Missing
Shipping Courier Status has 98 (1.7%) missing values Missing
Shipping Tracking Status has 98 (1.7%) missing values Missing
Cost Price is an unsupported type, check if it needs cleaning or further analysis Unsupported
Prepaid Amount has 5195 (88.7%) zeros Zeros
Discount has 1176 (20.1%) zeros Zeros
CGST has 4926 (84.1%) zeros Zeros
IGST has 934 (15.9%) zeros Zeros
SGST has 4926 (84.1%) zeros Zeros

Reproduction

Analysis started2025-03-01 10:33:59.680903
Analysis finished2025-03-01 10:34:43.810154
Duration44.13 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Shipping Address Id
Real number (ℝ)

High correlation 

Distinct5680
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19079918
Minimum17037893
Maximum20951988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2025-03-01T10:34:43.950856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum17037893
5-th percentile17307195
Q118267788
median18918412
Q320102973
95-th percentile20824217
Maximum20951988
Range3914095
Interquartile range (IQR)1835185

Descriptive statistics

Standard deviation1091279.9
Coefficient of variation (CV)0.05719521
Kurtosis-1.0743422
Mean19079918
Median Absolute Deviation (MAD)810727
Skewness0.11659087
Sum1.1180832 × 1011
Variance1.1908919 × 1012
MonotonicityNot monotonic
2025-03-01T10:34:44.162481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18030566 5
 
0.1%
17147660 4
 
0.1%
18634020 4
 
0.1%
18260913 4
 
0.1%
18603959 4
 
0.1%
18259525 4
 
0.1%
18409077 3
 
0.1%
18317699 3
 
0.1%
18504225 3
 
0.1%
18277293 3
 
0.1%
Other values (5670) 5823
99.4%
ValueCountFrequency (%)
17037893 1
< 0.1%
17038427 1
< 0.1%
17039694 1
< 0.1%
17048514 1
< 0.1%
17052967 1
< 0.1%
17055540 1
< 0.1%
17057290 1
< 0.1%
17058666 1
< 0.1%
17058904 1
< 0.1%
17059345 1
< 0.1%
ValueCountFrequency (%)
20951988 1
< 0.1%
20950770 1
< 0.1%
20949763 1
< 0.1%
20949385 1
< 0.1%
20949182 1
< 0.1%
20948869 1
< 0.1%
20948572 1
< 0.1%
20948430 1
< 0.1%
20948416 1
< 0.1%
20948258 1
< 0.1%

Category
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.4 KiB
Default
5860 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters41020
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDefault
2nd rowDefault
3rd rowDefault
4th rowDefault
5th rowDefault

Common Values

ValueCountFrequency (%)
Default 5860
100.0%

Length

2025-03-01T10:34:44.399876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:44.526735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
default 5860
100.0%

Most occurring characters

ValueCountFrequency (%)
D 5860
14.3%
e 5860
14.3%
f 5860
14.3%
a 5860
14.3%
u 5860
14.3%
l 5860
14.3%
t 5860
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35160
85.7%
Uppercase Letter 5860
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5860
16.7%
f 5860
16.7%
a 5860
16.7%
u 5860
16.7%
l 5860
16.7%
t 5860
16.7%
Uppercase Letter
ValueCountFrequency (%)
D 5860
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41020
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 5860
14.3%
e 5860
14.3%
f 5860
14.3%
a 5860
14.3%
u 5860
14.3%
l 5860
14.3%
t 5860
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 5860
14.3%
e 5860
14.3%
f 5860
14.3%
a 5860
14.3%
u 5860
14.3%
l 5860
14.3%
t 5860
14.3%
Distinct5680
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size414.4 KiB
2025-03-01T10:34:44.760112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length16
Mean length15.387201
Min length13

Characters and Unicode

Total characters90169
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5525 ?
Unique (%)94.3%

Sample

1st rowNIMAT25F01090
2nd rowNIMAT25F01091
3rd rowNIMAT25F01092
4th rowI0925NC000006459
5th rowI0925NC000006454
ValueCountFrequency (%)
i0925nc000004025 5
 
0.1%
i0925nc000005015 4
 
0.1%
i0925nc000004335 4
 
0.1%
i0925nc000004990 4
 
0.1%
i0925nc000004333 4
 
0.1%
i0925nc000003225 4
 
0.1%
i0925nc000004247 3
 
0.1%
i0925nc000005149 3
 
0.1%
nimat25f00854 3
 
0.1%
nimat25f00163 3
 
0.1%
Other values (5670) 5823
99.4%
2025-03-01T10:34:45.165036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 31769
35.2%
5 8687
 
9.6%
2 7702
 
8.5%
9 6377
 
7.1%
I 5860
 
6.5%
N 5860
 
6.5%
C 4663
 
5.2%
4 2825
 
3.1%
6 2763
 
3.1%
3 2536
 
2.8%
Other values (7) 11127
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68998
76.5%
Uppercase Letter 21171
 
23.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31769
46.0%
5 8687
 
12.6%
2 7702
 
11.2%
9 6377
 
9.2%
4 2825
 
4.1%
6 2763
 
4.0%
3 2536
 
3.7%
7 2507
 
3.6%
1 2138
 
3.1%
8 1694
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
I 5860
27.7%
N 5860
27.7%
C 4663
22.0%
M 1197
 
5.7%
A 1197
 
5.7%
T 1197
 
5.7%
F 1197
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 68998
76.5%
Latin 21171
 
23.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31769
46.0%
5 8687
 
12.6%
2 7702
 
11.2%
9 6377
 
9.2%
4 2825
 
4.1%
6 2763
 
4.0%
3 2536
 
3.7%
7 2507
 
3.6%
1 2138
 
3.1%
8 1694
 
2.5%
Latin
ValueCountFrequency (%)
I 5860
27.7%
N 5860
27.7%
C 4663
22.0%
M 1197
 
5.7%
A 1197
 
5.7%
T 1197
 
5.7%
F 1197
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90169
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31769
35.2%
5 8687
 
9.6%
2 7702
 
8.5%
9 6377
 
7.1%
I 5860
 
6.5%
N 5860
 
6.5%
C 4663
 
5.2%
4 2825
 
3.1%
6 2763
 
3.1%
3 2536
 
2.8%
Other values (7) 11127
 
12.3%
Distinct392
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size45.9 KiB
Minimum2024-01-10 09:59:00
Maximum2025-12-02 10:48:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-01T10:34:45.313885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:45.553723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct955
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size370.7 KiB
2025-03-01T10:34:45.920265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length18
Mean length7.7609215
Min length3

Characters and Unicode

Total characters45479
Distinct characters56
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique442 ?
Unique (%)7.5%

Sample

1st rowRaebareli
2nd rowPhagwara
3rd rowBerhampore
4th rowHyderabad
5th rowKolkata
ValueCountFrequency (%)
delhi 495
 
7.4%
new 330
 
4.9%
mumbai 330
 
4.9%
bangalore 280
 
4.2%
pune 200
 
3.0%
gurgaon 181
 
2.7%
noida 157
 
2.4%
hyderabad 147
 
2.2%
nagar 122
 
1.8%
bengaluru 118
 
1.8%
Other values (918) 4308
64.6%
2025-03-01T10:34:46.428982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8240
18.1%
r 3437
 
7.6%
u 2777
 
6.1%
i 2597
 
5.7%
e 2552
 
5.6%
n 2411
 
5.3%
h 2306
 
5.1%
d 1916
 
4.2%
l 1841
 
4.0%
o 1640
 
3.6%
Other values (46) 15762
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37624
82.7%
Uppercase Letter 6869
 
15.1%
Space Separator 879
 
1.9%
Other Punctuation 74
 
0.2%
Open Punctuation 10
 
< 0.1%
Close Punctuation 10
 
< 0.1%
Decimal Number 8
 
< 0.1%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8240
21.9%
r 3437
9.1%
u 2777
 
7.4%
i 2597
 
6.9%
e 2552
 
6.8%
n 2411
 
6.4%
h 2306
 
6.1%
d 1916
 
5.1%
l 1841
 
4.9%
o 1640
 
4.4%
Other values (15) 7907
21.0%
Uppercase Letter
ValueCountFrequency (%)
B 915
13.3%
N 800
11.6%
G 686
10.0%
D 627
9.1%
M 565
 
8.2%
P 430
 
6.3%
A 393
 
5.7%
K 383
 
5.6%
J 304
 
4.4%
S 282
 
4.1%
Other values (14) 1484
21.6%
Decimal Number
ValueCountFrequency (%)
2 4
50.0%
4 4
50.0%
Space Separator
ValueCountFrequency (%)
879
100.0%
Other Punctuation
ValueCountFrequency (%)
, 74
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44493
97.8%
Common 986
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8240
18.5%
r 3437
 
7.7%
u 2777
 
6.2%
i 2597
 
5.8%
e 2552
 
5.7%
n 2411
 
5.4%
h 2306
 
5.2%
d 1916
 
4.3%
l 1841
 
4.1%
o 1640
 
3.7%
Other values (39) 14776
33.2%
Common
ValueCountFrequency (%)
879
89.1%
, 74
 
7.5%
( 10
 
1.0%
) 10
 
1.0%
- 5
 
0.5%
2 4
 
0.4%
4 4
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45479
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8240
18.1%
r 3437
 
7.6%
u 2777
 
6.1%
i 2597
 
5.7%
e 2552
 
5.6%
n 2411
 
5.3%
h 2306
 
5.1%
d 1916
 
4.2%
l 1841
 
4.0%
o 1640
 
3.6%
Other values (46) 15762
34.7%

Shipping Address State
Categorical

High correlation 

Distinct35
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size380.8 KiB
Uttar Pradesh
935 
Maharashtra
830 
Delhi
495 
Karnataka
482 
Haryana
470 
Other values (30)
2648 

Length

Max length40
Median length17
Mean length9.5187713
Min length3

Characters and Unicode

Total characters55780
Distinct characters42
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowUttar Pradesh
2nd rowPunjab
3rd rowWest Bengal
4th rowTelangana
5th rowWest Bengal

Common Values

ValueCountFrequency (%)
Uttar Pradesh 935
16.0%
Maharashtra 830
14.2%
Delhi 495
8.4%
Karnataka 482
 
8.2%
Haryana 470
 
8.0%
Madhya Pradesh 340
 
5.8%
Gujarat 310
 
5.3%
Rajasthan 281
 
4.8%
Bihar 273
 
4.7%
West Bengal 215
 
3.7%
Other values (25) 1229
21.0%

Length

2025-03-01T10:34:46.604903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh 1390
18.1%
uttar 935
12.2%
maharashtra 830
10.8%
delhi 495
 
6.4%
karnataka 482
 
6.3%
haryana 470
 
6.1%
madhya 340
 
4.4%
gujarat 310
 
4.0%
rajasthan 281
 
3.7%
bihar 273
 
3.6%
Other values (36) 1877
24.4%

Most occurring characters

ValueCountFrequency (%)
a 13859
24.8%
r 6081
10.9%
h 5410
 
9.7%
t 4332
 
7.8%
s 3148
 
5.6%
e 2550
 
4.6%
n 2281
 
4.1%
d 2280
 
4.1%
1823
 
3.3%
P 1533
 
2.7%
Other values (32) 12483
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46277
83.0%
Uppercase Letter 7631
 
13.7%
Space Separator 1823
 
3.3%
Other Punctuation 49
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13859
29.9%
r 6081
13.1%
h 5410
 
11.7%
t 4332
 
9.4%
s 3148
 
6.8%
e 2550
 
5.5%
n 2281
 
4.9%
d 2280
 
4.9%
i 1235
 
2.7%
l 1093
 
2.4%
Other values (12) 4008
 
8.7%
Uppercase Letter
ValueCountFrequency (%)
P 1533
20.1%
M 1176
15.4%
U 1005
13.2%
K 577
 
7.6%
D 502
 
6.6%
H 499
 
6.5%
B 488
 
6.4%
G 338
 
4.4%
T 297
 
3.9%
R 281
 
3.7%
Other values (8) 935
12.3%
Space Separator
ValueCountFrequency (%)
1823
100.0%
Other Punctuation
ValueCountFrequency (%)
& 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53908
96.6%
Common 1872
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13859
25.7%
r 6081
11.3%
h 5410
 
10.0%
t 4332
 
8.0%
s 3148
 
5.8%
e 2550
 
4.7%
n 2281
 
4.2%
d 2280
 
4.2%
P 1533
 
2.8%
i 1235
 
2.3%
Other values (30) 11199
20.8%
Common
ValueCountFrequency (%)
1823
97.4%
& 49
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55780
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13859
24.8%
r 6081
10.9%
h 5410
 
9.7%
t 4332
 
7.8%
s 3148
 
5.6%
e 2550
 
4.6%
n 2281
 
4.1%
d 2280
 
4.1%
1823
 
3.3%
P 1533
 
2.7%
Other values (32) 12483
22.4%

Shipping Address Country
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size337.8 KiB
IN
5860 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters11720
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIN
2nd rowIN
3rd rowIN
4th rowIN
5th rowIN

Common Values

ValueCountFrequency (%)
IN 5860
100.0%

Length

2025-03-01T10:34:46.767907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:46.853244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
in 5860
100.0%

Most occurring characters

ValueCountFrequency (%)
I 5860
50.0%
N 5860
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11720
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 5860
50.0%
N 5860
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11720
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 5860
50.0%
N 5860
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 5860
50.0%
N 5860
50.0%

Shipping Address Pincode
Real number (ℝ)

High correlation 

Distinct2104
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean395816.75
Minimum110001
Maximum855101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2025-03-01T10:34:46.981157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum110001
5-th percentile110060
Q1201306
median400064
Q3560024
95-th percentile803214.05
Maximum855101
Range745100
Interquartile range (IQR)358718

Descriptive statistics

Standard deviation218087.94
Coefficient of variation (CV)0.55098209
Kurtosis-0.78969982
Mean395816.75
Median Absolute Deviation (MAD)172063
Skewness0.43807072
Sum2.3194861 × 109
Variance4.7562349 × 1010
MonotonicityNot monotonic
2025-03-01T10:34:47.184732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201301 105
 
1.8%
122001 75
 
1.3%
122002 36
 
0.6%
401107 35
 
0.6%
201306 34
 
0.6%
110085 33
 
0.6%
560037 27
 
0.5%
122003 25
 
0.4%
560068 25
 
0.4%
560035 24
 
0.4%
Other values (2094) 5441
92.8%
ValueCountFrequency (%)
110001 3
 
0.1%
110002 1
 
< 0.1%
110003 3
 
0.1%
110005 7
 
0.1%
110007 12
0.2%
110008 13
0.2%
110009 18
0.3%
110010 2
 
< 0.1%
110012 2
 
< 0.1%
110014 2
 
< 0.1%
ValueCountFrequency (%)
855101 2
< 0.1%
854327 1
< 0.1%
854326 1
< 0.1%
854318 2
< 0.1%
854311 2
< 0.1%
854304 1
< 0.1%
854301 1
< 0.1%
854205 2
< 0.1%
854109 1
< 0.1%
854105 1
< 0.1%

Billing Address Id
Real number (ℝ)

High correlation 

Distinct5680
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19079918
Minimum17037894
Maximum20951989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2025-03-01T10:34:47.384820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum17037894
5-th percentile17307195
Q118267788
median18918412
Q320102973
95-th percentile20824217
Maximum20951989
Range3914095
Interquartile range (IQR)1835185

Descriptive statistics

Standard deviation1091279.9
Coefficient of variation (CV)0.057195206
Kurtosis-1.0743422
Mean19079918
Median Absolute Deviation (MAD)810727
Skewness0.1165908
Sum1.1180832 × 1011
Variance1.1908918 × 1012
MonotonicityNot monotonic
2025-03-01T10:34:48.080611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18030566 5
 
0.1%
17147660 4
 
0.1%
18634020 4
 
0.1%
18260913 4
 
0.1%
18603959 4
 
0.1%
18259525 4
 
0.1%
18409077 3
 
0.1%
18317699 3
 
0.1%
18504226 3
 
0.1%
18277293 3
 
0.1%
Other values (5670) 5823
99.4%
ValueCountFrequency (%)
17037894 1
< 0.1%
17038428 1
< 0.1%
17039695 1
< 0.1%
17048515 1
< 0.1%
17052967 1
< 0.1%
17055540 1
< 0.1%
17057290 1
< 0.1%
17058667 1
< 0.1%
17058904 1
< 0.1%
17059345 1
< 0.1%
ValueCountFrequency (%)
20951989 1
< 0.1%
20950770 1
< 0.1%
20949763 1
< 0.1%
20949385 1
< 0.1%
20949182 1
< 0.1%
20948869 1
< 0.1%
20948572 1
< 0.1%
20948430 1
< 0.1%
20948416 1
< 0.1%
20948258 1
< 0.1%
Distinct955
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size370.7 KiB
2025-03-01T10:34:48.447147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length18
Mean length7.7609215
Min length3

Characters and Unicode

Total characters45479
Distinct characters56
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique442 ?
Unique (%)7.5%

Sample

1st rowRaebareli
2nd rowPhagwara
3rd rowBerhampore
4th rowHyderabad
5th rowKolkata
ValueCountFrequency (%)
delhi 495
 
7.4%
new 330
 
4.9%
mumbai 330
 
4.9%
bangalore 280
 
4.2%
pune 200
 
3.0%
gurgaon 181
 
2.7%
noida 157
 
2.4%
hyderabad 147
 
2.2%
nagar 122
 
1.8%
bengaluru 118
 
1.8%
Other values (918) 4308
64.6%
2025-03-01T10:34:48.980226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8240
18.1%
r 3437
 
7.6%
u 2777
 
6.1%
i 2597
 
5.7%
e 2552
 
5.6%
n 2411
 
5.3%
h 2306
 
5.1%
d 1916
 
4.2%
l 1841
 
4.0%
o 1640
 
3.6%
Other values (46) 15762
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37624
82.7%
Uppercase Letter 6869
 
15.1%
Space Separator 879
 
1.9%
Other Punctuation 74
 
0.2%
Open Punctuation 10
 
< 0.1%
Close Punctuation 10
 
< 0.1%
Decimal Number 8
 
< 0.1%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8240
21.9%
r 3437
9.1%
u 2777
 
7.4%
i 2597
 
6.9%
e 2552
 
6.8%
n 2411
 
6.4%
h 2306
 
6.1%
d 1916
 
5.1%
l 1841
 
4.9%
o 1640
 
4.4%
Other values (15) 7907
21.0%
Uppercase Letter
ValueCountFrequency (%)
B 915
13.3%
N 800
11.6%
G 686
10.0%
D 627
9.1%
M 565
 
8.2%
P 430
 
6.3%
A 393
 
5.7%
K 383
 
5.6%
J 304
 
4.4%
S 282
 
4.1%
Other values (14) 1484
21.6%
Decimal Number
ValueCountFrequency (%)
2 4
50.0%
4 4
50.0%
Space Separator
ValueCountFrequency (%)
879
100.0%
Other Punctuation
ValueCountFrequency (%)
, 74
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44493
97.8%
Common 986
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8240
18.5%
r 3437
 
7.7%
u 2777
 
6.2%
i 2597
 
5.8%
e 2552
 
5.7%
n 2411
 
5.4%
h 2306
 
5.2%
d 1916
 
4.3%
l 1841
 
4.1%
o 1640
 
3.7%
Other values (39) 14776
33.2%
Common
ValueCountFrequency (%)
879
89.1%
, 74
 
7.5%
( 10
 
1.0%
) 10
 
1.0%
- 5
 
0.5%
2 4
 
0.4%
4 4
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45479
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8240
18.1%
r 3437
 
7.6%
u 2777
 
6.1%
i 2597
 
5.7%
e 2552
 
5.6%
n 2411
 
5.3%
h 2306
 
5.1%
d 1916
 
4.2%
l 1841
 
4.0%
o 1640
 
3.6%
Other values (46) 15762
34.7%

Billing Address State
Categorical

High correlation 

Distinct35
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size380.8 KiB
Uttar Pradesh
935 
Maharashtra
830 
Delhi
495 
Karnataka
482 
Haryana
470 
Other values (30)
2648 

Length

Max length40
Median length17
Mean length9.5187713
Min length3

Characters and Unicode

Total characters55780
Distinct characters42
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowUttar Pradesh
2nd rowPunjab
3rd rowWest Bengal
4th rowTelangana
5th rowWest Bengal

Common Values

ValueCountFrequency (%)
Uttar Pradesh 935
16.0%
Maharashtra 830
14.2%
Delhi 495
8.4%
Karnataka 482
 
8.2%
Haryana 470
 
8.0%
Madhya Pradesh 340
 
5.8%
Gujarat 310
 
5.3%
Rajasthan 281
 
4.8%
Bihar 273
 
4.7%
West Bengal 215
 
3.7%
Other values (25) 1229
21.0%

Length

2025-03-01T10:34:49.144498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh 1390
18.1%
uttar 935
12.2%
maharashtra 830
10.8%
delhi 495
 
6.4%
karnataka 482
 
6.3%
haryana 470
 
6.1%
madhya 340
 
4.4%
gujarat 310
 
4.0%
rajasthan 281
 
3.7%
bihar 273
 
3.6%
Other values (36) 1877
24.4%

Most occurring characters

ValueCountFrequency (%)
a 13859
24.8%
r 6081
10.9%
h 5410
 
9.7%
t 4332
 
7.8%
s 3148
 
5.6%
e 2550
 
4.6%
n 2281
 
4.1%
d 2280
 
4.1%
1823
 
3.3%
P 1533
 
2.7%
Other values (32) 12483
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46277
83.0%
Uppercase Letter 7631
 
13.7%
Space Separator 1823
 
3.3%
Other Punctuation 49
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13859
29.9%
r 6081
13.1%
h 5410
 
11.7%
t 4332
 
9.4%
s 3148
 
6.8%
e 2550
 
5.5%
n 2281
 
4.9%
d 2280
 
4.9%
i 1235
 
2.7%
l 1093
 
2.4%
Other values (12) 4008
 
8.7%
Uppercase Letter
ValueCountFrequency (%)
P 1533
20.1%
M 1176
15.4%
U 1005
13.2%
K 577
 
7.6%
D 502
 
6.6%
H 499
 
6.5%
B 488
 
6.4%
G 338
 
4.4%
T 297
 
3.9%
R 281
 
3.7%
Other values (8) 935
12.3%
Space Separator
ValueCountFrequency (%)
1823
100.0%
Other Punctuation
ValueCountFrequency (%)
& 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53908
96.6%
Common 1872
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13859
25.7%
r 6081
11.3%
h 5410
 
10.0%
t 4332
 
8.0%
s 3148
 
5.8%
e 2550
 
4.7%
n 2281
 
4.2%
d 2280
 
4.2%
P 1533
 
2.8%
i 1235
 
2.3%
Other values (30) 11199
20.8%
Common
ValueCountFrequency (%)
1823
97.4%
& 49
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55780
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13859
24.8%
r 6081
10.9%
h 5410
 
9.7%
t 4332
 
7.8%
s 3148
 
5.6%
e 2550
 
4.6%
n 2281
 
4.1%
d 2280
 
4.1%
1823
 
3.3%
P 1533
 
2.7%
Other values (32) 12483
22.4%

Billing Address Country
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size337.8 KiB
IN
5860 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters11720
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIN
2nd rowIN
3rd rowIN
4th rowIN
5th rowIN

Common Values

ValueCountFrequency (%)
IN 5860
100.0%

Length

2025-03-01T10:34:49.310170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:49.394114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
in 5860
100.0%

Most occurring characters

ValueCountFrequency (%)
I 5860
50.0%
N 5860
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11720
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 5860
50.0%
N 5860
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11720
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 5860
50.0%
N 5860
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 5860
50.0%
N 5860
50.0%

Billing Address Pincode
Real number (ℝ)

High correlation 

Distinct2104
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean395816.75
Minimum110001
Maximum855101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2025-03-01T10:34:49.523844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum110001
5-th percentile110060
Q1201306
median400064
Q3560024
95-th percentile803214.05
Maximum855101
Range745100
Interquartile range (IQR)358718

Descriptive statistics

Standard deviation218087.94
Coefficient of variation (CV)0.55098209
Kurtosis-0.78969982
Mean395816.75
Median Absolute Deviation (MAD)172063
Skewness0.43807072
Sum2.3194861 × 109
Variance4.7562349 × 1010
MonotonicityNot monotonic
2025-03-01T10:34:49.741466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201301 105
 
1.8%
122001 75
 
1.3%
122002 36
 
0.6%
401107 35
 
0.6%
201306 34
 
0.6%
110085 33
 
0.6%
560037 27
 
0.5%
122003 25
 
0.4%
560068 25
 
0.4%
560035 24
 
0.4%
Other values (2094) 5441
92.8%
ValueCountFrequency (%)
110001 3
 
0.1%
110002 1
 
< 0.1%
110003 3
 
0.1%
110005 7
 
0.1%
110007 12
0.2%
110008 13
0.2%
110009 18
0.3%
110010 2
 
< 0.1%
110012 2
 
< 0.1%
110014 2
 
< 0.1%
ValueCountFrequency (%)
855101 2
< 0.1%
854327 1
< 0.1%
854326 1
< 0.1%
854318 2
< 0.1%
854311 2
< 0.1%
854304 1
< 0.1%
854301 1
< 0.1%
854205 2
< 0.1%
854109 1
< 0.1%
854105 1
< 0.1%

Shipping Method
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size343.5 KiB
STD
5860 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17580
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTD
2nd rowSTD
3rd rowSTD
4th rowSTD
5th rowSTD

Common Values

ValueCountFrequency (%)
STD 5860
100.0%

Length

2025-03-01T10:34:49.923146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:50.009450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
std 5860
100.0%

Most occurring characters

ValueCountFrequency (%)
S 5860
33.3%
T 5860
33.3%
D 5860
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17580
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 5860
33.3%
T 5860
33.3%
D 5860
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 17580
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 5860
33.3%
T 5860
33.3%
D 5860
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 5860
33.3%
T 5860
33.3%
D 5860
33.3%
Distinct440
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size393.3 KiB
2025-03-01T10:34:50.260483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length16
Mean length11.706655
Min length9

Characters and Unicode

Total characters68601
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)1.2%

Sample

1st rowADGRN035-M
2nd rowADKSET151-2XL
3rd rowADKSET147-L
4th rowADYLW036-S
5th rowADKSET261-L
ValueCountFrequency (%)
adcrdset159-s 125
 
2.1%
adcrdset159-m 120
 
2.0%
adkset157-s 89
 
1.5%
adjmp145-s 87
 
1.5%
adkset148-m 87
 
1.5%
adkset157-m 84
 
1.4%
adcrdset159-l 83
 
1.4%
adcrdset159-xs 81
 
1.4%
adkset261-s 80
 
1.4%
adkset148-l 73
 
1.2%
Other values (430) 4951
84.5%
2025-03-01T10:34:50.723397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 7450
 
10.9%
A 6529
 
9.5%
S 6388
 
9.3%
- 5860
 
8.5%
1 4335
 
6.3%
T 4245
 
6.2%
E 3976
 
5.8%
L 3021
 
4.4%
K 2729
 
4.0%
2 2666
 
3.9%
Other values (23) 21402
31.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 44797
65.3%
Decimal Number 17944
26.2%
Dash Punctuation 5860
 
8.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 7450
16.6%
A 6529
14.6%
S 6388
14.3%
T 4245
9.5%
E 3976
8.9%
L 3021
6.7%
K 2729
 
6.1%
X 2180
 
4.9%
M 1809
 
4.0%
R 1796
 
4.0%
Other values (12) 4674
10.4%
Decimal Number
ValueCountFrequency (%)
1 4335
24.2%
2 2666
14.9%
0 2237
12.5%
5 1981
11.0%
6 1694
 
9.4%
4 1492
 
8.3%
7 1019
 
5.7%
8 875
 
4.9%
9 855
 
4.8%
3 790
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 5860
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44797
65.3%
Common 23804
34.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 7450
16.6%
A 6529
14.6%
S 6388
14.3%
T 4245
9.5%
E 3976
8.9%
L 3021
6.7%
K 2729
 
6.1%
X 2180
 
4.9%
M 1809
 
4.0%
R 1796
 
4.0%
Other values (12) 4674
10.4%
Common
ValueCountFrequency (%)
- 5860
24.6%
1 4335
18.2%
2 2666
11.2%
0 2237
 
9.4%
5 1981
 
8.3%
6 1694
 
7.1%
4 1492
 
6.3%
7 1019
 
4.3%
8 875
 
3.7%
9 855
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 7450
 
10.9%
A 6529
 
9.5%
S 6388
 
9.3%
- 5860
 
8.5%
1 4335
 
6.3%
T 4245
 
6.2%
E 3976
 
5.8%
L 3021
 
4.4%
K 2729
 
4.0%
2 2666
 
3.9%
Other values (23) 21402
31.2%
Distinct91
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size639.7 KiB
2025-03-01T10:34:51.002170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length89
Median length77
Mean length54.766041
Min length18

Characters and Unicode

Total characters320929
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st rowWomen Panelled Gotta Patti Chanderi Silk Kurta with Palazzos & With Dupatta
2nd rowEthnic Motifs Yoke Design Gotta Patti Chanderi Silk Kurta with Churidar & Dupatta
3rd rowWomen Embroidered Regular Thread Work Chanderi Cotton Kurta with Churidar & With Dupatta
4th rowLeheriya Embroidered Empire Beads and Stones Silk Crepe Kurta Churidar & Dupatta
5th rowWomen Ethnic Motifs Panelled Sequinned Chanderi Cotton Kurta with Churidar & With Dupatta
ValueCountFrequency (%)
with 6046
 
12.7%
kurta 3423
 
7.2%
2900
 
6.1%
dupatta 2725
 
5.7%
embroidered 1970
 
4.1%
women 1864
 
3.9%
churidar 1857
 
3.9%
regular 1494
 
3.1%
ethnic 1025
 
2.1%
sequinned 957
 
2.0%
Other values (94) 23469
49.2%
2025-03-01T10:34:51.528508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41870
 
13.0%
r 25494
 
7.9%
t 24652
 
7.7%
a 24399
 
7.6%
e 23982
 
7.5%
i 20749
 
6.5%
o 14730
 
4.6%
u 13561
 
4.2%
d 12746
 
4.0%
h 12224
 
3.8%
Other values (42) 106522
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 230976
72.0%
Uppercase Letter 43374
 
13.5%
Space Separator 41870
 
13.0%
Other Punctuation 2903
 
0.9%
Dash Punctuation 1603
 
0.5%
Decimal Number 203
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 25494
11.0%
t 24652
10.7%
a 24399
10.6%
e 23982
10.4%
i 20749
9.0%
o 14730
 
6.4%
u 13561
 
5.9%
d 12746
 
5.5%
h 12224
 
5.3%
n 11731
 
5.1%
Other values (15) 46708
20.2%
Uppercase Letter
ValueCountFrequency (%)
W 6644
15.3%
C 5130
11.8%
D 4088
9.4%
S 3983
9.2%
E 3743
8.6%
K 3592
8.3%
P 3378
7.8%
T 2560
 
5.9%
R 1926
 
4.4%
O 1087
 
2.5%
Other values (12) 7243
16.7%
Other Punctuation
ValueCountFrequency (%)
& 2900
99.9%
, 3
 
0.1%
Space Separator
ValueCountFrequency (%)
41870
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1603
100.0%
Decimal Number
ValueCountFrequency (%)
3 203
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 274350
85.5%
Common 46579
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 25494
 
9.3%
t 24652
 
9.0%
a 24399
 
8.9%
e 23982
 
8.7%
i 20749
 
7.6%
o 14730
 
5.4%
u 13561
 
4.9%
d 12746
 
4.6%
h 12224
 
4.5%
n 11731
 
4.3%
Other values (37) 90082
32.8%
Common
ValueCountFrequency (%)
41870
89.9%
& 2900
 
6.2%
- 1603
 
3.4%
3 203
 
0.4%
, 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 320929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41870
 
13.0%
r 25494
 
7.9%
t 24652
 
7.7%
a 24399
 
7.6%
e 23982
 
7.5%
i 20749
 
6.5%
o 14730
 
4.6%
u 13561
 
4.2%
d 12746
 
4.0%
h 12224
 
3.8%
Other values (42) 106522
33.2%

Item Type Color
Categorical

High correlation  Missing 

Distinct10
Distinct (%)1.1%
Missing4961
Missing (%)84.7%
Memory size365.4 KiB
Burgandy
336 
Cream
216 
Green
215 
Red
105 
Teal
 
10
Other values (5)
 
17

Length

Max length9
Median length8
Mean length5.8809789
Min length3

Characters and Unicode

Total characters5287
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowGreen
2nd rowCream
3rd rowCream
4th rowCream
5th rowBurgandy

Common Values

ValueCountFrequency (%)
Burgandy 336
 
5.7%
Cream 216
 
3.7%
Green 215
 
3.7%
Red 105
 
1.8%
Teal 10
 
0.2%
WHITE 9
 
0.2%
BLACK 3
 
0.1%
BEIGE 3
 
0.1%
Navy Blue 1
 
< 0.1%
GREEN 1
 
< 0.1%
(Missing) 4961
84.7%

Length

2025-03-01T10:34:51.667732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:51.824368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
burgandy 336
37.3%
cream 216
24.0%
green 216
24.0%
red 105
 
11.7%
teal 10
 
1.1%
white 9
 
1.0%
black 3
 
0.3%
beige 3
 
0.3%
navy 1
 
0.1%
blue 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 767
14.5%
e 762
14.4%
a 563
10.6%
n 551
10.4%
d 441
8.3%
B 343
6.5%
u 337
6.4%
y 337
6.4%
g 336
6.4%
C 219
 
4.1%
Other values (15) 631
11.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4322
81.7%
Uppercase Letter 964
 
18.2%
Space Separator 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 343
35.6%
C 219
22.7%
G 219
22.7%
R 106
 
11.0%
T 19
 
2.0%
E 17
 
1.8%
I 12
 
1.2%
W 9
 
0.9%
H 9
 
0.9%
L 3
 
0.3%
Other values (3) 8
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
r 767
17.7%
e 762
17.6%
a 563
13.0%
n 551
12.7%
d 441
10.2%
u 337
7.8%
y 337
7.8%
g 336
7.8%
m 216
 
5.0%
l 11
 
0.3%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5286
> 99.9%
Common 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 767
14.5%
e 762
14.4%
a 563
10.7%
n 551
10.4%
d 441
8.3%
B 343
6.5%
u 337
6.4%
y 337
6.4%
g 336
6.4%
C 219
 
4.1%
Other values (14) 630
11.9%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 767
14.5%
e 762
14.4%
a 563
10.6%
n 551
10.4%
d 441
8.3%
B 343
6.5%
u 337
6.4%
y 337
6.4%
g 336
6.4%
C 219
 
4.1%
Other values (15) 631
11.9%

Channel Name
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size387.1 KiB
MYNTRAPPMP
4663 
NYKAA_FASHION
1197 

Length

Max length13
Median length10
Mean length10.612799
Min length10

Characters and Unicode

Total characters62191
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNYKAA_FASHION
2nd rowNYKAA_FASHION
3rd rowNYKAA_FASHION
4th rowMYNTRAPPMP
5th rowMYNTRAPPMP

Common Values

ValueCountFrequency (%)
MYNTRAPPMP 4663
79.6%
NYKAA_FASHION 1197
 
20.4%

Length

2025-03-01T10:34:52.022475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:52.157165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
myntrappmp 4663
79.6%
nykaa_fashion 1197
 
20.4%

Most occurring characters

ValueCountFrequency (%)
P 13989
22.5%
M 9326
15.0%
A 8254
13.3%
N 7057
11.3%
Y 5860
9.4%
T 4663
 
7.5%
R 4663
 
7.5%
K 1197
 
1.9%
_ 1197
 
1.9%
F 1197
 
1.9%
Other values (4) 4788
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 60994
98.1%
Connector Punctuation 1197
 
1.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 13989
22.9%
M 9326
15.3%
A 8254
13.5%
N 7057
11.6%
Y 5860
9.6%
T 4663
 
7.6%
R 4663
 
7.6%
K 1197
 
2.0%
F 1197
 
2.0%
S 1197
 
2.0%
Other values (3) 3591
 
5.9%
Connector Punctuation
ValueCountFrequency (%)
_ 1197
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 60994
98.1%
Common 1197
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 13989
22.9%
M 9326
15.3%
A 8254
13.5%
N 7057
11.6%
Y 5860
9.6%
T 4663
 
7.6%
R 4663
 
7.6%
K 1197
 
2.0%
F 1197
 
2.0%
S 1197
 
2.0%
Other values (3) 3591
 
5.9%
Common
ValueCountFrequency (%)
_ 1197
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 13989
22.5%
M 9326
15.0%
A 8254
13.3%
N 7057
11.3%
Y 5860
9.4%
T 4663
 
7.5%
R 4663
 
7.5%
K 1197
 
1.9%
_ 1197
 
1.9%
F 1197
 
1.9%
Other values (4) 4788
 
7.7%

Gift Wrap
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
False
5860 
ValueCountFrequency (%)
False 5860
100.0%
2025-03-01T10:34:52.283563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

HSN Code
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing43
Missing (%)0.7%
Memory size383.4 KiB
62045300.0
5817 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters58170
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row62045300.0
2nd row62045300.0
3rd row62045300.0
4th row62045300.0
5th row62045300.0

Common Values

ValueCountFrequency (%)
62045300.0 5817
99.3%
(Missing) 43
 
0.7%

Length

2025-03-01T10:34:52.524499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:52.759626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
62045300.0 5817
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23268
40.0%
6 5817
 
10.0%
2 5817
 
10.0%
4 5817
 
10.0%
5 5817
 
10.0%
3 5817
 
10.0%
. 5817
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52353
90.0%
Other Punctuation 5817
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23268
44.4%
6 5817
 
11.1%
2 5817
 
11.1%
4 5817
 
11.1%
5 5817
 
11.1%
3 5817
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 5817
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58170
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23268
40.0%
6 5817
 
10.0%
2 5817
 
10.0%
4 5817
 
10.0%
5 5817
 
10.0%
3 5817
 
10.0%
. 5817
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23268
40.0%
6 5817
 
10.0%
2 5817
 
10.0%
4 5817
 
10.0%
5 5817
 
10.0%
3 5817
 
10.0%
. 5817
 
10.0%

MRP
Real number (ℝ)

High correlation  Missing 

Distinct32
Distinct (%)0.6%
Missing188
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean5329.8062
Minimum1599
Maximum12999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2025-03-01T10:34:52.976093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1599
5-th percentile3499
Q14299
median4999
Q36499
95-th percentile6999
Maximum12999
Range11400
Interquartile range (IQR)2200

Descriptive statistics

Standard deviation1311.6218
Coefficient of variation (CV)0.24609183
Kurtosis1.5761615
Mean5329.8062
Median Absolute Deviation (MAD)1000
Skewness0.76161937
Sum30230661
Variance1720351.7
MonotonicityNot monotonic
2025-03-01T10:34:53.322469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3999 884
15.1%
4999 638
10.9%
6499 548
9.4%
5999 423
 
7.2%
6599 404
 
6.9%
4499 342
 
5.8%
6299 320
 
5.5%
6999 303
 
5.2%
4599 282
 
4.8%
5499 252
 
4.3%
Other values (22) 1276
21.8%
ValueCountFrequency (%)
1599 1
 
< 0.1%
1899 3
 
0.1%
2799 12
 
0.2%
2999 51
 
0.9%
3299 118
 
2.0%
3499 216
 
3.7%
3599 37
 
0.6%
3999 884
15.1%
4099 4
 
0.1%
4122 1
 
< 0.1%
ValueCountFrequency (%)
12999 2
 
< 0.1%
10999 41
 
0.7%
8899 26
 
0.4%
7999 134
 
2.3%
7500 3
 
0.1%
7499 37
 
0.6%
6999 303
5.2%
6599 404
6.9%
6499 548
9.4%
6299 320
5.5%

Total Price
Real number (ℝ)

High correlation 

Distinct279
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3424.9193
Minimum879
Maximum8999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2025-03-01T10:34:53.663900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum879
5-th percentile2239
Q12639
median3330
Q34199
95-th percentile4639
Maximum8999
Range8120
Interquartile range (IQR)1560

Descriptive statistics

Standard deviation919.10866
Coefficient of variation (CV)0.26835922
Kurtosis1.5546684
Mean3424.9193
Median Absolute Deviation (MAD)761
Skewness0.48309537
Sum20070027
Variance844760.73
MonotonicityNot monotonic
2025-03-01T10:34:54.054961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3299 278
 
4.7%
3899 270
 
4.6%
4487 249
 
4.2%
2519 238
 
4.1%
4283 168
 
2.9%
2309 134
 
2.3%
4289 114
 
1.9%
4211 108
 
1.8%
3127 105
 
1.8%
2719 103
 
1.8%
Other values (269) 4093
69.8%
ValueCountFrequency (%)
879 1
 
< 0.1%
1006 2
 
< 0.1%
1045 1
 
< 0.1%
1199 2
 
< 0.1%
1200 1
 
< 0.1%
1253 6
 
0.1%
1254 4
 
0.1%
1319 34
0.6%
1320 11
 
0.2%
1379 7
 
0.1%
ValueCountFrequency (%)
8999 1
 
< 0.1%
7699 4
 
0.1%
7480 2
 
< 0.1%
7479 34
0.6%
6999 1
 
< 0.1%
6499 1
 
< 0.1%
6230 1
 
< 0.1%
5999 3
 
0.1%
5599 3
 
0.1%
5399 1
 
< 0.1%

Selling Price
Real number (ℝ)

High correlation 

Distinct279
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3424.9193
Minimum879
Maximum8999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2025-03-01T10:34:54.413323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum879
5-th percentile2239
Q12639
median3330
Q34199
95-th percentile4639
Maximum8999
Range8120
Interquartile range (IQR)1560

Descriptive statistics

Standard deviation919.10866
Coefficient of variation (CV)0.26835922
Kurtosis1.5546684
Mean3424.9193
Median Absolute Deviation (MAD)761
Skewness0.48309537
Sum20070027
Variance844760.73
MonotonicityNot monotonic
2025-03-01T10:34:54.828972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3299 278
 
4.7%
3899 270
 
4.6%
4487 249
 
4.2%
2519 238
 
4.1%
4283 168
 
2.9%
2309 134
 
2.3%
4289 114
 
1.9%
4211 108
 
1.8%
3127 105
 
1.8%
2719 103
 
1.8%
Other values (269) 4093
69.8%
ValueCountFrequency (%)
879 1
 
< 0.1%
1006 2
 
< 0.1%
1045 1
 
< 0.1%
1199 2
 
< 0.1%
1200 1
 
< 0.1%
1253 6
 
0.1%
1254 4
 
0.1%
1319 34
0.6%
1320 11
 
0.2%
1379 7
 
0.1%
ValueCountFrequency (%)
8999 1
 
< 0.1%
7699 4
 
0.1%
7480 2
 
< 0.1%
7479 34
0.6%
6999 1
 
< 0.1%
6499 1
 
< 0.1%
6230 1
 
< 0.1%
5999 3
 
0.1%
5599 3
 
0.1%
5399 1
 
< 0.1%

Cost Price
Unsupported

Missing  Rejected  Unsupported 

Missing5860
Missing (%)100.0%
Memory size45.9 KiB

Prepaid Amount
Real number (ℝ)

High correlation  Zeros 

Distinct445
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean347.20062
Minimum0
Maximum7450
Zeros5195
Zeros (%)88.7%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2025-03-01T10:34:55.160023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3059.205
Maximum7450
Range7450
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1030.5199
Coefficient of variation (CV)2.9680818
Kurtosis7.7425593
Mean347.20062
Median Absolute Deviation (MAD)0
Skewness2.9465252
Sum2034595.7
Variance1061971.2
MonotonicityNot monotonic
2025-03-01T10:34:55.368057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5195
88.7%
2520 17
 
0.3%
2240 10
 
0.2%
2640 9
 
0.2%
4440 9
 
0.2%
3500 9
 
0.2%
4800 8
 
0.1%
4488 7
 
0.1%
2800 6
 
0.1%
4560 6
 
0.1%
Other values (435) 584
 
10.0%
ValueCountFrequency (%)
0 5195
88.7%
0.23 1
 
< 0.1%
11.5 1
 
< 0.1%
50 1
 
< 0.1%
99 1
 
< 0.1%
190.5 2
 
< 0.1%
228.87 1
 
< 0.1%
239 1
 
< 0.1%
281 1
 
< 0.1%
301 1
 
< 0.1%
ValueCountFrequency (%)
7450 1
 
< 0.1%
7250 1
 
< 0.1%
5340 3
0.1%
5310 1
 
< 0.1%
5228 1
 
< 0.1%
5200 1
 
< 0.1%
5191.3 1
 
< 0.1%
5020 1
 
< 0.1%
4999 1
 
< 0.1%
4862 1
 
< 0.1%

Subtotal
Real number (ℝ)

High correlation 

Distinct403
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4597.0289
Minimum995.24
Maximum10197.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2025-03-01T10:34:55.562785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum995.24
5-th percentile2250
Q13707.68
median4285.71
Q35853.61
95-th percentile6624.11
Maximum10197.68
Range9202.44
Interquartile range (IQR)2145.93

Descriptive statistics

Standard deviation1440.8683
Coefficient of variation (CV)0.31343468
Kurtosis0.31872993
Mean4597.0289
Median Absolute Deviation (MAD)1041.6
Skewness0.34239951
Sum26938589
Variance2076101.3
MonotonicityNot monotonic
2025-03-01T10:34:55.757895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4645.54 278
 
4.7%
6081.25 220
 
3.8%
6118.25 201
 
3.4%
5840.11 143
 
2.4%
3729.11 124
 
2.1%
5039.46 114
 
1.9%
4947.82 108
 
1.8%
4263.96 105
 
1.8%
3707.68 103
 
1.8%
3995.04 102
 
1.7%
Other values (393) 4362
74.4%
ValueCountFrequency (%)
995.24 1
 
< 0.1%
1071.43 1
 
< 0.1%
1119.64 4
 
0.1%
1178.57 11
0.2%
1275 2
 
< 0.1%
1285.71 2
 
< 0.1%
1285.72 1
 
< 0.1%
1325 5
0.1%
1392.86 3
 
0.1%
1399.12 1
 
< 0.1%
ValueCountFrequency (%)
10197.68 34
0.6%
10174.11 4
 
0.1%
8346.04 8
 
0.1%
8326.96 8
 
0.1%
8034.82 1
 
< 0.1%
7656.25 1
 
< 0.1%
7656.24 3
 
0.1%
7570.54 5
 
0.1%
7527.68 6
 
0.1%
7501.96 14
0.2%

Discount
Real number (ℝ)

High correlation  Zeros 

Distinct162
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1539.0243
Minimum0
Maximum4800
Zeros1176
Zeros (%)20.1%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2025-03-01T10:34:55.974698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11120
median1548
Q32112
95-th percentile3290
Maximum4800
Range4800
Interquartile range (IQR)992

Descriptive statistics

Standard deviation1033.9396
Coefficient of variation (CV)0.67181499
Kurtosis-0.35090255
Mean1539.0243
Median Absolute Deviation (MAD)564
Skewness0.10588915
Sum9018682.1
Variance1069031
MonotonicityNot monotonic
2025-03-01T10:34:56.181387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1176
 
20.1%
2600 278
 
4.7%
1700 278
 
4.7%
2112 249
 
4.2%
2016 168
 
2.9%
1480 143
 
2.4%
1980 130
 
2.2%
1560 121
 
2.1%
1210 114
 
1.9%
1190 110
 
1.9%
Other values (152) 3093
52.8%
ValueCountFrequency (%)
0 1176
20.1%
48.64 1
 
< 0.1%
76.7 1
 
< 0.1%
78.83 1
 
< 0.1%
80 82
 
1.4%
135.73 1
 
< 0.1%
151.59 1
 
< 0.1%
175 2
 
< 0.1%
206.53 1
 
< 0.1%
217.06 1
 
< 0.1%
ValueCountFrequency (%)
4800 4
 
0.1%
4410 22
0.4%
4340 39
0.7%
4200 3
 
0.1%
4000 5
 
0.1%
3738 8
 
0.1%
3660 4
 
0.1%
3600 6
 
0.1%
3560 8
 
0.1%
3540 12
 
0.2%

GST Tax Type Code
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing1591
Missing (%)27.2%
Memory size358.0 KiB
GST12
4269 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters21345
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGST12
2nd rowGST12
3rd rowGST12
4th rowGST12
5th rowGST12

Common Values

ValueCountFrequency (%)
GST12 4269
72.8%
(Missing) 1591
 
27.2%

Length

2025-03-01T10:34:56.360245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:56.440259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gst12 4269
100.0%

Most occurring characters

ValueCountFrequency (%)
G 4269
20.0%
S 4269
20.0%
T 4269
20.0%
1 4269
20.0%
2 4269
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12807
60.0%
Decimal Number 8538
40.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 4269
33.3%
S 4269
33.3%
T 4269
33.3%
Decimal Number
ValueCountFrequency (%)
1 4269
50.0%
2 4269
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12807
60.0%
Common 8538
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 4269
33.3%
S 4269
33.3%
T 4269
33.3%
Common
ValueCountFrequency (%)
1 4269
50.0%
2 4269
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 4269
20.0%
S 4269
20.0%
T 4269
20.0%
1 4269
20.0%
2 4269
20.0%

CGST
Real number (ℝ)

High correlation  Zeros 

Distinct177
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.502348
Minimum0
Maximum400.66
Zeros4926
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2025-03-01T10:34:56.565167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile209.94
Maximum400.66
Range400.66
Interquartile range (IQR)0

Descriptive statistics

Standard deviation70.832179
Coefficient of variation (CV)2.4008997
Kurtosis3.9107197
Mean29.502348
Median Absolute Deviation (MAD)0
Skewness2.2410892
Sum172883.76
Variance5017.1976
MonotonicityNot monotonic
2025-03-01T10:34:56.773436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4926
84.1%
176.73 57
 
1.0%
208.88 50
 
0.9%
240.38 48
 
0.8%
134.94 31
 
0.5%
229.44 25
 
0.4%
167.52 23
 
0.4%
171.38 22
 
0.4%
151.98 21
 
0.4%
183.16 18
 
0.3%
Other values (167) 639
 
10.9%
ValueCountFrequency (%)
0 4926
84.1%
20.93 1
 
< 0.1%
67.12 1
 
< 0.1%
67.18 1
 
< 0.1%
70.66 4
 
0.1%
73.88 1
 
< 0.1%
76.44 1
 
< 0.1%
77.09 4
 
0.1%
77.14 1
 
< 0.1%
79.44 1
 
< 0.1%
ValueCountFrequency (%)
400.66 13
0.2%
321.38 2
 
< 0.1%
299.94 1
 
< 0.1%
286.07 1
 
< 0.1%
286.02 2
 
< 0.1%
278.57 1
 
< 0.1%
276.48 2
 
< 0.1%
267.8 1
 
< 0.1%
257.14 8
0.1%
257.09 6
0.1%

IGST
Real number (ℝ)

High correlation  Zeros 

Distinct273
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.90994
Minimum0
Maximum964.18
Zeros934
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2025-03-01T10:34:56.999555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1254.89
median335.14
Q3437.04
95-th percentile494.89
Maximum964.18
Range964.18
Interquartile range (IQR)182.15

Descriptive statistics

Standard deviation161.22802
Coefficient of variation (CV)0.5236207
Kurtosis-0.030473274
Mean307.90994
Median Absolute Deviation (MAD)87.75
Skewness-0.66341944
Sum1804352.2
Variance25994.473
MonotonicityNot monotonic
2025-03-01T10:34:57.196658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 934
 
15.9%
353.46 221
 
3.8%
417.75 220
 
3.8%
269.89 207
 
3.5%
480.75 199
 
3.4%
458.89 143
 
2.4%
247.39 116
 
2.0%
459.54 99
 
1.7%
451.18 92
 
1.6%
291.32 88
 
1.5%
Other values (263) 3541
60.4%
ValueCountFrequency (%)
0 934
15.9%
47.9 2
 
< 0.1%
49.76 1
 
< 0.1%
128.46 2
 
< 0.1%
128.57 1
 
< 0.1%
134.25 5
 
0.1%
134.36 3
 
0.1%
141.32 30
 
0.5%
141.43 11
 
0.2%
147.75 6
 
0.1%
ValueCountFrequency (%)
964.18 1
 
< 0.1%
824.89 4
 
0.1%
801.43 2
 
< 0.1%
801.32 21
0.4%
749.89 1
 
< 0.1%
696.32 1
 
< 0.1%
667.5 1
 
< 0.1%
642.75 1
 
< 0.1%
599.89 2
 
< 0.1%
578.46 1
 
< 0.1%

SGST
Real number (ℝ)

High correlation  Zeros 

Distinct177
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.502348
Minimum0
Maximum400.66
Zeros4926
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2025-03-01T10:34:57.384536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile209.94
Maximum400.66
Range400.66
Interquartile range (IQR)0

Descriptive statistics

Standard deviation70.832179
Coefficient of variation (CV)2.4008997
Kurtosis3.9107197
Mean29.502348
Median Absolute Deviation (MAD)0
Skewness2.2410892
Sum172883.76
Variance5017.1976
MonotonicityNot monotonic
2025-03-01T10:34:57.598206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4926
84.1%
176.73 57
 
1.0%
208.88 50
 
0.9%
240.38 48
 
0.8%
134.94 31
 
0.5%
229.44 25
 
0.4%
167.52 23
 
0.4%
171.38 22
 
0.4%
151.98 21
 
0.4%
183.16 18
 
0.3%
Other values (167) 639
 
10.9%
ValueCountFrequency (%)
0 4926
84.1%
20.93 1
 
< 0.1%
67.12 1
 
< 0.1%
67.18 1
 
< 0.1%
70.66 4
 
0.1%
73.88 1
 
< 0.1%
76.44 1
 
< 0.1%
77.09 4
 
0.1%
77.14 1
 
< 0.1%
79.44 1
 
< 0.1%
ValueCountFrequency (%)
400.66 13
0.2%
321.38 2
 
< 0.1%
299.94 1
 
< 0.1%
286.07 1
 
< 0.1%
286.02 2
 
< 0.1%
278.57 1
 
< 0.1%
276.48 2
 
< 0.1%
267.8 1
 
< 0.1%
257.14 8
0.1%
257.09 6
0.1%

UTGST
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size332.0 KiB
0
5860 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5860
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5860
100.0%

Length

2025-03-01T10:34:57.779922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:57.866808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 5860
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5860
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5860
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5860
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5860
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5860
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5860
100.0%

CESS
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size332.0 KiB
0
5860 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5860
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5860
100.0%

Length

2025-03-01T10:34:57.988930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:58.077383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 5860
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5860
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5860
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5860
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5860
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5860
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5860
100.0%

CGST Rate
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size343.5 KiB
0.0
4926 
6.0
933 
2.5
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17580
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row6.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4926
84.1%
6.0 933
 
15.9%
2.5 1
 
< 0.1%

Length

2025-03-01T10:34:58.183744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:58.284342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4926
84.1%
6.0 933
 
15.9%
2.5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 10785
61.3%
. 5860
33.3%
6 933
 
5.3%
2 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11720
66.7%
Other Punctuation 5860
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10785
92.0%
6 933
 
8.0%
2 1
 
< 0.1%
5 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 5860
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17580
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10785
61.3%
. 5860
33.3%
6 933
 
5.3%
2 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10785
61.3%
. 5860
33.3%
6 933
 
5.3%
2 1
 
< 0.1%
5 1
 
< 0.1%

IGST Rate
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size336.9 KiB
12
4923 
0
934 
5
 
3

Length

Max length2
Median length2
Mean length1.8401024
Min length1

Characters and Unicode

Total characters10783
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row12
3rd row12
4th row12
5th row12

Common Values

ValueCountFrequency (%)
12 4923
84.0%
0 934
 
15.9%
5 3
 
0.1%

Length

2025-03-01T10:34:58.416050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:58.517874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
12 4923
84.0%
0 934
 
15.9%
5 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 4923
45.7%
2 4923
45.7%
0 934
 
8.7%
5 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10783
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4923
45.7%
2 4923
45.7%
0 934
 
8.7%
5 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 10783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4923
45.7%
2 4923
45.7%
0 934
 
8.7%
5 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4923
45.7%
2 4923
45.7%
0 934
 
8.7%
5 3
 
< 0.1%

SGST Rate
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size343.5 KiB
0.0
4926 
6.0
933 
2.5
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17580
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row6.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4926
84.1%
6.0 933
 
15.9%
2.5 1
 
< 0.1%

Length

2025-03-01T10:34:58.643727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:58.744836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4926
84.1%
6.0 933
 
15.9%
2.5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 10785
61.3%
. 5860
33.3%
6 933
 
5.3%
2 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11720
66.7%
Other Punctuation 5860
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10785
92.0%
6 933
 
8.0%
2 1
 
< 0.1%
5 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 5860
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17580
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10785
61.3%
. 5860
33.3%
6 933
 
5.3%
2 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10785
61.3%
. 5860
33.3%
6 933
 
5.3%
2 1
 
< 0.1%
5 1
 
< 0.1%

UTGST Rate
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size332.0 KiB
0
5860 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5860
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5860
100.0%

Length

2025-03-01T10:34:58.871265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:58.963486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 5860
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5860
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5860
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5860
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5860
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5860
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5860
100.0%

CESS Rate
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size332.0 KiB
0
5860 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5860
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5860
100.0%

Length

2025-03-01T10:34:59.085616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:59.169266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 5860
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5860
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5860
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5860
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5860
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5860
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5860
100.0%

Sale Order Status
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size372.1 KiB
COMPLETE
5860 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters46880
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOMPLETE
2nd rowCOMPLETE
3rd rowCOMPLETE
4th rowCOMPLETE
5th rowCOMPLETE

Common Values

ValueCountFrequency (%)
COMPLETE 5860
100.0%

Length

2025-03-01T10:34:59.276448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:59.367279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
complete 5860
100.0%

Most occurring characters

ValueCountFrequency (%)
E 11720
25.0%
C 5860
12.5%
O 5860
12.5%
M 5860
12.5%
P 5860
12.5%
L 5860
12.5%
T 5860
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 46880
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 11720
25.0%
C 5860
12.5%
O 5860
12.5%
M 5860
12.5%
P 5860
12.5%
L 5860
12.5%
T 5860
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 46880
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 11720
25.0%
C 5860
12.5%
O 5860
12.5%
M 5860
12.5%
P 5860
12.5%
L 5860
12.5%
T 5860
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 11720
25.0%
C 5860
12.5%
O 5860
12.5%
M 5860
12.5%
P 5860
12.5%
L 5860
12.5%
T 5860
12.5%

GSTIN
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size412.2 KiB
09AAICN9819MIZL
5860 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters87900
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row09AAICN9819MIZL
2nd row09AAICN9819MIZL
3rd row09AAICN9819MIZL
4th row09AAICN9819MIZL
5th row09AAICN9819MIZL

Common Values

ValueCountFrequency (%)
09AAICN9819MIZL 5860
100.0%

Length

2025-03-01T10:34:59.471193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:34:59.559379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
09aaicn9819mizl 5860
100.0%

Most occurring characters

ValueCountFrequency (%)
9 17580
20.0%
A 11720
13.3%
I 11720
13.3%
0 5860
 
6.7%
C 5860
 
6.7%
N 5860
 
6.7%
8 5860
 
6.7%
1 5860
 
6.7%
M 5860
 
6.7%
Z 5860
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 52740
60.0%
Decimal Number 35160
40.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 11720
22.2%
I 11720
22.2%
C 5860
11.1%
N 5860
11.1%
M 5860
11.1%
Z 5860
11.1%
L 5860
11.1%
Decimal Number
ValueCountFrequency (%)
9 17580
50.0%
0 5860
 
16.7%
8 5860
 
16.7%
1 5860
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 52740
60.0%
Common 35160
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 11720
22.2%
I 11720
22.2%
C 5860
11.1%
N 5860
11.1%
M 5860
11.1%
Z 5860
11.1%
L 5860
11.1%
Common
ValueCountFrequency (%)
9 17580
50.0%
0 5860
 
16.7%
8 5860
 
16.7%
1 5860
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 17580
20.0%
A 11720
13.3%
I 11720
13.3%
0 5860
 
6.7%
C 5860
 
6.7%
N 5860
 
6.7%
8 5860
 
6.7%
1 5860
 
6.7%
M 5860
 
6.7%
Z 5860
 
6.7%
Distinct91
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size639.7 KiB
2025-03-01T10:34:59.828223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length89
Median length77
Mean length54.766041
Min length18

Characters and Unicode

Total characters320929
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st rowWomen Panelled Gotta Patti Chanderi Silk Kurta with Palazzos & With Dupatta
2nd rowEthnic Motifs Yoke Design Gotta Patti Chanderi Silk Kurta with Churidar & Dupatta
3rd rowWomen Embroidered Regular Thread Work Chanderi Cotton Kurta with Churidar & With Dupatta
4th rowLeheriya Embroidered Empire Beads and Stones Silk Crepe Kurta Churidar & Dupatta
5th rowWomen Ethnic Motifs Panelled Sequinned Chanderi Cotton Kurta with Churidar & With Dupatta
ValueCountFrequency (%)
with 6046
 
12.7%
kurta 3423
 
7.2%
2900
 
6.1%
dupatta 2725
 
5.7%
embroidered 1970
 
4.1%
women 1864
 
3.9%
churidar 1857
 
3.9%
regular 1494
 
3.1%
ethnic 1025
 
2.1%
sequinned 957
 
2.0%
Other values (94) 23469
49.2%
2025-03-01T10:35:00.369687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41870
 
13.0%
r 25494
 
7.9%
t 24652
 
7.7%
a 24399
 
7.6%
e 23982
 
7.5%
i 20749
 
6.5%
o 14730
 
4.6%
u 13561
 
4.2%
d 12746
 
4.0%
h 12224
 
3.8%
Other values (42) 106522
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 230976
72.0%
Uppercase Letter 43374
 
13.5%
Space Separator 41870
 
13.0%
Other Punctuation 2903
 
0.9%
Dash Punctuation 1603
 
0.5%
Decimal Number 203
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 25494
11.0%
t 24652
10.7%
a 24399
10.6%
e 23982
10.4%
i 20749
9.0%
o 14730
 
6.4%
u 13561
 
5.9%
d 12746
 
5.5%
h 12224
 
5.3%
n 11731
 
5.1%
Other values (15) 46708
20.2%
Uppercase Letter
ValueCountFrequency (%)
W 6644
15.3%
C 5130
11.8%
D 4088
9.4%
S 3983
9.2%
E 3743
8.6%
K 3592
8.3%
P 3378
7.8%
T 2560
 
5.9%
R 1926
 
4.4%
O 1087
 
2.5%
Other values (12) 7243
16.7%
Other Punctuation
ValueCountFrequency (%)
& 2900
99.9%
, 3
 
0.1%
Space Separator
ValueCountFrequency (%)
41870
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1603
100.0%
Decimal Number
ValueCountFrequency (%)
3 203
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 274350
85.5%
Common 46579
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 25494
 
9.3%
t 24652
 
9.0%
a 24399
 
8.9%
e 23982
 
8.7%
i 20749
 
7.6%
o 14730
 
5.4%
u 13561
 
4.9%
d 12746
 
4.6%
h 12224
 
4.5%
n 11731
 
4.3%
Other values (37) 90082
32.8%
Common
ValueCountFrequency (%)
41870
89.9%
& 2900
 
6.2%
- 1603
 
3.4%
3 203
 
0.4%
, 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 320929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41870
 
13.0%
r 25494
 
7.9%
t 24652
 
7.7%
a 24399
 
7.6%
e 23982
 
7.5%
i 20749
 
6.5%
o 14730
 
4.6%
u 13561
 
4.2%
d 12746
 
4.0%
h 12224
 
3.8%
Other values (42) 106522
33.2%
Distinct490
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size393.4 KiB
2025-03-01T10:35:00.624825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length16
Mean length11.723891
Min length9

Characters and Unicode

Total characters68702
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)1.5%

Sample

1st rowADGRN035-M
2nd rowADKSET151-2XL
3rd rowADKSET147-L
4th rowADYLW036-S
5th rowADKSET261-L
ValueCountFrequency (%)
adcrdset159-s 125
 
2.1%
adcrdset159-m 120
 
2.0%
adkset148-m 87
 
1.5%
adcrdset159-l 83
 
1.4%
adkset157-s 81
 
1.4%
adkset157-m 81
 
1.4%
adcrdset159-xs 81
 
1.4%
adkset261-s 80
 
1.4%
adjmp145-s 76
 
1.3%
adkset148-l 73
 
1.2%
Other values (480) 4973
84.9%
2025-03-01T10:35:01.068876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 7450
 
10.8%
A 6779
 
9.9%
S 6292
 
9.2%
- 5921
 
8.6%
1 4431
 
6.4%
T 4149
 
6.0%
E 3880
 
5.6%
L 3021
 
4.4%
K 2729
 
4.0%
2 2644
 
3.8%
Other values (23) 21406
31.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 44859
65.3%
Decimal Number 17922
 
26.1%
Dash Punctuation 5921
 
8.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 7450
16.6%
A 6779
15.1%
S 6292
14.0%
T 4149
9.2%
E 3880
8.6%
L 3021
6.7%
K 2729
 
6.1%
X 2202
 
4.9%
M 1809
 
4.0%
R 1796
 
4.0%
Other values (12) 4752
10.6%
Decimal Number
ValueCountFrequency (%)
1 4431
24.7%
2 2644
14.8%
0 2333
13.0%
5 1981
11.1%
6 1598
 
8.9%
4 1492
 
8.3%
7 1019
 
5.7%
8 875
 
4.9%
9 855
 
4.8%
3 694
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 5921
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44859
65.3%
Common 23843
34.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 7450
16.6%
A 6779
15.1%
S 6292
14.0%
T 4149
9.2%
E 3880
8.6%
L 3021
6.7%
K 2729
 
6.1%
X 2202
 
4.9%
M 1809
 
4.0%
R 1796
 
4.0%
Other values (12) 4752
10.6%
Common
ValueCountFrequency (%)
- 5921
24.8%
1 4431
18.6%
2 2644
11.1%
0 2333
 
9.8%
5 1981
 
8.3%
6 1598
 
6.7%
4 1492
 
6.3%
7 1019
 
4.3%
8 875
 
3.7%
9 855
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68702
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 7450
 
10.8%
A 6779
 
9.9%
S 6292
 
9.2%
- 5921
 
8.6%
1 4431
 
6.4%
T 4149
 
6.0%
E 3880
 
5.6%
L 3021
 
4.4%
K 2729
 
4.0%
2 2644
 
3.8%
Other values (23) 21406
31.2%

Shipping Courier Status
Categorical

High correlation  Imbalance  Missing 

Distinct10
Distinct (%)0.2%
Missing98
Missing (%)1.7%
Memory size390.8 KiB
DELIVERED
4815 
COURIER_RETURN-DELIVERED
679 
COURIER_RETURN-RTO-DELIVERED
 
156
SHIPPED
 
69
COURIER_RETURN-CONFIRMED
 
22
Other values (5)
 
21

Length

Max length28
Median length9
Mean length11.333565
Min length7

Characters and Unicode

Total characters65304
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDELIVERED
2nd rowDELIVERED
3rd rowDELIVERED
4th rowDELIVERED
5th rowDELIVERED

Common Values

ValueCountFrequency (%)
DELIVERED 4815
82.2%
COURIER_RETURN-DELIVERED 679
 
11.6%
COURIER_RETURN-RTO-DELIVERED 156
 
2.7%
SHIPPED 69
 
1.2%
COURIER_RETURN-CONFIRMED 22
 
0.4%
OUTFORDELIVERY 9
 
0.2%
INTRANSIT 7
 
0.1%
SHIPMENTHELD 2
 
< 0.1%
COURIER_RETURN-RTO-INTRANSIT 2
 
< 0.1%
COURIER_RETURN-RTO-FAILED 1
 
< 0.1%
(Missing) 98
 
1.7%

Length

2025-03-01T10:35:01.211422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:35:01.348036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
delivered 4815
83.6%
courier_return-delivered 679
 
11.8%
courier_return-rto-delivered 156
 
2.7%
shipped 69
 
1.2%
courier_return-confirmed 22
 
0.4%
outfordelivery 9
 
0.2%
intransit 7
 
0.1%
shipmentheld 2
 
< 0.1%
courier_return-rto-intransit 2
 
< 0.1%
courier_return-rto-failed 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 18784
28.8%
D 11403
17.5%
R 9298
14.2%
I 6631
 
10.2%
L 5662
 
8.7%
V 5659
 
8.7%
U 1729
 
2.6%
O 1059
 
1.6%
T 1048
 
1.6%
- 1019
 
1.6%
Other values (10) 3012
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 63425
97.1%
Dash Punctuation 1019
 
1.6%
Connector Punctuation 860
 
1.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 18784
29.6%
D 11403
18.0%
R 9298
14.7%
I 6631
 
10.5%
L 5662
 
8.9%
V 5659
 
8.9%
U 1729
 
2.7%
O 1059
 
1.7%
T 1048
 
1.7%
N 902
 
1.4%
Other values (8) 1250
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 1019
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 860
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63425
97.1%
Common 1879
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 18784
29.6%
D 11403
18.0%
R 9298
14.7%
I 6631
 
10.5%
L 5662
 
8.9%
V 5659
 
8.9%
U 1729
 
2.7%
O 1059
 
1.7%
T 1048
 
1.7%
N 902
 
1.4%
Other values (8) 1250
 
2.0%
Common
ValueCountFrequency (%)
- 1019
54.2%
_ 860
45.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 18784
28.8%
D 11403
17.5%
R 9298
14.2%
I 6631
 
10.2%
L 5662
 
8.7%
V 5659
 
8.7%
U 1729
 
2.6%
O 1059
 
1.6%
T 1048
 
1.6%
- 1019
 
1.6%
Other values (10) 3012
 
4.6%

Shipping Tracking Status
Categorical

High correlation  Missing 

Distinct5
Distinct (%)0.1%
Missing98
Missing (%)1.7%
Memory size423.3 KiB
STATUS_NOT_DEFINED
4112 
DELIVERED
933 
RTO_DELIVERED_TO_SELLER
679 
RTO_INITIATED
 
22
PENDING_IN_TRANSIT
 
16

Length

Max length23
Median length18
Mean length17.112808
Min length9

Characters and Unicode

Total characters98604
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDELIVERED
2nd rowDELIVERED
3rd rowDELIVERED
4th rowSTATUS_NOT_DEFINED
5th rowSTATUS_NOT_DEFINED

Common Values

ValueCountFrequency (%)
STATUS_NOT_DEFINED 4112
70.2%
DELIVERED 933
 
15.9%
RTO_DELIVERED_TO_SELLER 679
 
11.6%
RTO_INITIATED 22
 
0.4%
PENDING_IN_TRANSIT 16
 
0.3%
(Missing) 98
 
1.7%

Length

2025-03-01T10:35:01.547493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T10:35:01.664123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
status_not_defined 4112
71.4%
delivered 933
 
16.2%
rto_delivered_to_seller 679
 
11.8%
rto_initiated 22
 
0.4%
pending_in_transit 16
 
0.3%

Most occurring characters

ValueCountFrequency (%)
E 14456
14.7%
T 13792
14.0%
D 11486
11.6%
_ 10315
10.5%
S 8919
9.0%
N 8310
8.4%
I 5838
5.9%
O 5492
 
5.6%
A 4150
 
4.2%
U 4112
 
4.2%
Other values (6) 11734
11.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 88289
89.5%
Connector Punctuation 10315
 
10.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 14456
16.4%
T 13792
15.6%
D 11486
13.0%
S 8919
10.1%
N 8310
9.4%
I 5838
6.6%
O 5492
 
6.2%
A 4150
 
4.7%
U 4112
 
4.7%
F 4112
 
4.7%
Other values (5) 7622
8.6%
Connector Punctuation
ValueCountFrequency (%)
_ 10315
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88289
89.5%
Common 10315
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 14456
16.4%
T 13792
15.6%
D 11486
13.0%
S 8919
10.1%
N 8310
9.4%
I 5838
6.6%
O 5492
 
6.2%
A 4150
 
4.7%
U 4112
 
4.7%
F 4112
 
4.7%
Other values (5) 7622
8.6%
Common
ValueCountFrequency (%)
_ 10315
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98604
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 14456
14.7%
T 13792
14.0%
D 11486
11.6%
_ 10315
10.5%
S 8919
9.0%
N 8310
8.4%
I 5838
5.9%
O 5492
 
5.6%
A 4150
 
4.2%
U 4112
 
4.2%
Other values (6) 11734
11.9%

Interactions

2025-03-01T10:34:39.174923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:04.136687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:07.991358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:11.938707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:16.732542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:19.388698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:21.550816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:23.951116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:26.893930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:30.015857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:32.141295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:34.318056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:36.971125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:39.422236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:04.365576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:08.290824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:12.382843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:16.918011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:19.561647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:21.734679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:24.133514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:27.194956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:30.180897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:32.339471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:34.501533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:37.153033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:39.735212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:04.568673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:08.482718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:12.716845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:17.095301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:19.730458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:21.916401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:24.339672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:27.520844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:30.369621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:32.505785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:34.675542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:37.320580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:40.040493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:04.764757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:08.742279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:13.032336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:17.327650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:19.896560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:22.094584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:24.521691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:27.832973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:30.529980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:32.672131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:34.858827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:37.495659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:40.341277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:04.946146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:08.990309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:13.576731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:17.754129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:20.066741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:22.286787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:24.721433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:28.150784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:30.685695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:32.837320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:35.034914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:37.657073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:40.627385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:05.113511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:09.319769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:14.034823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:17.915772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:20.211786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:22.446721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:25.190440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:28.454249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:30.832897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:33.012076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:35.587581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:37.817676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:40.925331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:05.391957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:09.607399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:14.571828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:18.098774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:20.420288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:22.642330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:25.384428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:28.783585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:31.009073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:33.174680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:35.764081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:37.993460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:41.194902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:05.778671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:09.922243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:15.106810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:18.309799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:20.583174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:22.860032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:25.564013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:29.030769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:31.169368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:33.367374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:35.947145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:38.175763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:41.433231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:06.021173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:10.264364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:15.773944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:18.504148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:20.747593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:23.037058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:25.756481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:29.193923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:31.370408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:33.529993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:36.133639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:38.372533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:41.717837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:06.418719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:10.563029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:16.012705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:18.663854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:20.890685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:23.197186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:25.917878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:29.357277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:31.510659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:33.674378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:36.296768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:38.517568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:41.969762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:07.118825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:10.870492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:16.180589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:18.836022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:21.040209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:23.404107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:26.142039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:29.518631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:31.654679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:33.825108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:36.458049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:38.670645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:42.168798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:07.415650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:11.212021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:16.386686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:19.010132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:21.199289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:23.605484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:26.454587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:29.684736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:31.815795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:34.000489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:36.631973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:38.831857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:42.349601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:07.663417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:11.553724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:16.551113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:19.177988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:21.381152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:23.774218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:26.657927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:29.851933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:31.979598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:34.151205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:36.803344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T10:34:38.982250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-01T10:35:01.818323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Billing Address IdBilling Address PincodeBilling Address StateCGSTCGST RateChannel NameDiscountIGSTIGST RateItem Type ColorMRPPrepaid AmountSGSTSGST RateSelling PriceShipping Address IdShipping Address PincodeShipping Address StateShipping Courier StatusShipping Tracking StatusSubtotalTotal Price
Billing Address Id1.0000.0130.0760.0230.0590.1340.1720.0710.0570.1550.164-0.0680.0230.0590.1261.0000.0130.0760.0910.1190.1730.126
Billing Address Pincode0.0131.0000.878-0.3180.6370.0310.0210.1510.6380.057-0.034-0.035-0.3180.637-0.0630.0131.0000.8780.0260.024-0.024-0.063
Billing Address State0.0760.8781.0000.3240.7020.0910.0530.3350.7030.1160.0410.0000.3240.7020.0260.0760.8781.0000.0460.0630.0660.026
CGST0.023-0.3180.3241.0000.7060.0980.038-0.6320.7060.3200.047-0.0201.0000.7060.0430.023-0.3180.3240.0000.0340.0490.043
CGST Rate0.0590.6370.7020.7061.0000.0050.2660.7050.7070.0390.3530.0000.7061.0000.0570.0590.6370.7020.0800.0000.1160.057
Channel Name0.1340.0310.0910.0980.0051.0000.9510.1380.0060.1840.1440.6970.0980.0050.1070.1340.0310.0910.3900.8740.6970.107
Discount0.1720.0210.0530.0380.2660.9511.0000.1280.3080.4960.472-0.4900.0380.2660.2150.1720.0210.0530.1260.4160.8330.215
IGST0.0710.1510.335-0.6320.7050.1380.1281.0000.7060.5520.548-0.000-0.6320.7050.7070.0710.1510.3350.0580.0830.4560.707
IGST Rate0.0570.6380.7030.7060.7070.0060.3080.7061.0000.0390.6120.0840.7060.7070.1020.0570.6380.7030.0000.0000.2010.102
Item Type Color0.1550.0570.1160.3200.0390.1840.4960.5520.0391.0000.6110.1460.3200.0390.7020.1550.0570.1160.0790.1500.4910.702
MRP0.164-0.0340.0410.0470.3530.1440.4720.5480.6120.6111.000-0.0030.0470.3530.7880.164-0.0340.0410.0420.0600.7720.788
Prepaid Amount-0.068-0.0350.000-0.0200.0000.697-0.490-0.0000.0840.146-0.0031.000-0.0200.000-0.028-0.068-0.0350.0000.0510.364-0.400-0.028
SGST0.023-0.3180.3241.0000.7060.0980.038-0.6320.7060.3200.047-0.0201.0000.7060.0430.023-0.3180.3240.0000.0340.0490.043
SGST Rate0.0590.6370.7020.7061.0000.0050.2660.7050.7070.0390.3530.0000.7061.0000.0570.0590.6370.7020.0800.0000.1160.057
Selling Price0.126-0.0630.0260.0430.0570.1070.2150.7070.1020.7020.788-0.0280.0430.0571.0000.126-0.0630.0260.0430.0400.6671.000
Shipping Address Id1.0000.0130.0760.0230.0590.1340.1720.0710.0570.1550.164-0.0680.0230.0590.1261.0000.0130.0760.0910.1190.1730.126
Shipping Address Pincode0.0131.0000.878-0.3180.6370.0310.0210.1510.6380.057-0.034-0.035-0.3180.637-0.0630.0131.0000.8780.0260.024-0.024-0.063
Shipping Address State0.0760.8781.0000.3240.7020.0910.0530.3350.7030.1160.0410.0000.3240.7020.0260.0760.8781.0000.0460.0630.0660.026
Shipping Courier Status0.0910.0260.0460.0000.0800.3900.1260.0580.0000.0790.0420.0510.0000.0800.0430.0910.0260.0461.0000.8670.0990.043
Shipping Tracking Status0.1190.0240.0630.0340.0000.8740.4160.0830.0000.1500.0600.3640.0340.0000.0400.1190.0240.0630.8671.0000.2990.040
Subtotal0.173-0.0240.0660.0490.1160.6970.8330.4560.2010.4910.772-0.4000.0490.1160.6670.173-0.0240.0660.0990.2991.0000.667
Total Price0.126-0.0630.0260.0430.0570.1070.2150.7070.1020.7020.788-0.0280.0430.0571.0000.126-0.0630.0260.0430.0400.6671.000

Missing values

2025-03-01T10:34:42.745414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-01T10:34:43.201172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-01T10:34:43.632483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Shipping Address IdCategoryInvoice CodeInvoice CreatedShipping Address CityShipping Address StateShipping Address CountryShipping Address PincodeBilling Address IdBilling Address CityBilling Address StateBilling Address CountryBilling Address PincodeShipping MethodItem SKU CodeItem Type NameItem Type ColorChannel NameGift WrapHSN CodeMRPTotal PriceSelling PriceCost PricePrepaid AmountSubtotalDiscountGST Tax Type CodeCGSTIGSTSGSTUTGSTCESSCGST RateIGST RateSGST RateUTGST RateCESS RateSale Order StatusGSTINSKU NameSeller SKU CodeShipping Courier StatusShipping Tracking Status
019970813DefaultNIMAT25F0109031/12/2024 09:45RaebareliUttar PradeshIN22900119970814RaebareliUttar PradeshIN229001STDADGRN035-MWomen Panelled Gotta Patti Chanderi Silk Kurta with Palazzos & With DupattaGreenNYKAA_FASHIONFalse62045300.05999.02700.02700.0NaN2670.02710.72300.0NaN144.640.00144.64006.006.000COMPLETE09AAICN9819MIZLWomen Panelled Gotta Patti Chanderi Silk Kurta with Palazzos & With DupattaADGRN035-MDELIVEREDDELIVERED
119984900DefaultNIMAT25F0109131/12/2024 09:45PhagwaraPunjabIN14441019984901PhagwaraPunjabIN144410STDADKSET151-2XLEthnic Motifs Yoke Design Gotta Patti Chanderi Silk Kurta with Churidar & DupattaNaNNYKAA_FASHIONFalse62045300.06599.04356.04356.0NaN0.03889.290.0GST120.00466.710.00000.0120.000COMPLETE09AAICN9819MIZLEthnic Motifs Yoke Design Gotta Patti Chanderi Silk Kurta with Churidar & DupattaADKSET151-2XLDELIVEREDDELIVERED
219986570DefaultNIMAT25F0109231/12/2024 09:45BerhamporeWest BengalIN74212219986571BerhamporeWest BengalIN742122STDADKSET147-LWomen Embroidered Regular Thread Work Chanderi Cotton Kurta with Churidar & With DupattaCreamNYKAA_FASHIONFalse62045300.06999.03500.03500.0NaN0.03125.000.0NaN0.00375.000.00000.0120.000COMPLETE09AAICN9819MIZLWomen Embroidered Regular Thread Work Chanderi Cotton Kurta with Churidar & With DupattaADKSET147-LDELIVEREDDELIVERED
319987349DefaultI0925NC00000645931/12/2024 09:44HyderabadTelanganaIN50007519987349HyderabadTelanganaIN500075STDADYLW036-SLeheriya Embroidered Empire Beads and Stones Silk Crepe Kurta Churidar & DupattaNaNMYNTRAPPMPFalse62045300.06499.03639.03639.0NaN0.06109.112860.0GST120.00389.890.00000.0120.000COMPLETE09AAICN9819MIZLLeheriya Embroidered Empire Beads and Stones Silk Crepe Kurta Churidar & DupattaADYLW036-SDELIVEREDSTATUS_NOT_DEFINED
419971790DefaultI0925NC00000645431/12/2024 09:44KolkataWest BengalIN70011519971790KolkataWest BengalIN700115STDADKSET261-LWomen Ethnic Motifs Panelled Sequinned Chanderi Cotton Kurta with Churidar & With DupattaNaNMYNTRAPPMPFalse62045300.06299.04283.04283.0NaN0.05840.112016.0GST120.00458.890.00000.0120.000COMPLETE09AAICN9819MIZLWomen Ethnic Motifs Panelled Sequinned Chanderi Cotton Kurta with Churidar & With DupattaADKSET261-LDELIVEREDSTATUS_NOT_DEFINED
519972169DefaultI0925NC00000646131/12/2024 09:44JaipurRajasthanIN30201519972169JaipurRajasthanIN302015STDADKSET147-XSWomen Embroidered Regular Thread Work Chanderi Cotton Kurta with Churidar & With DupattaCreamMYNTRAPPMPFalse62045300.06999.03499.03499.0NaN0.06624.113500.0NaN0.00374.890.00000.0120.000COMPLETE09AAICN9819MIZLWomen Embroidered Regular Thread Work Chanderi Cotton Kurta with Churidar & With DupattaADKSET147A-XSDELIVEREDSTATUS_NOT_DEFINED
619977759DefaultI0925NC00000646231/12/2024 09:44SuratGujaratIN39500419977759SuratGujaratIN395004STDADCRDSET163-SEmbroidered Round Neck Top With PalazzosNaNMYNTRAPPMPFalse62045300.05499.04289.04289.0NaN0.05039.461210.0GST120.00459.540.00000.0120.000COMPLETE09AAICN9819MIZLEmbroidered Round Neck Top With PalazzosADCRDSET163-SDELIVEREDSTATUS_NOT_DEFINED
719978570DefaultI0925NC00000645331/12/2024 09:44New DelhiDelhiIN11009219978570New DelhiDelhiIN110092STDADKSET147-XLWomen Embroidered Regular Thread Work Chanderi Cotton Kurta with Churidar & With DupattaCreamMYNTRAPPMPFalse62045300.06999.03499.03499.0NaN0.06624.113500.0NaN0.00374.890.00000.0120.000COMPLETE09AAICN9819MIZLWomen Embroidered Regular Thread Work Chanderi Cotton Kurta with Churidar & With DupattaADKSET147A-XLDELIVEREDSTATUS_NOT_DEFINED
819981812DefaultI0925NC00000645531/12/2024 09:44SuratGujaratIN39500419981812SuratGujaratIN395004STDADCRDSET159-LBrocade Co-Ord SetNaNMYNTRAPPMPFalse62045300.04999.03299.03299.0NaN0.04645.541700.0GST120.00353.460.00000.0120.000COMPLETE09AAICN9819MIZLBrocade Co-Ord SetADCRDSET159-LDELIVEREDSTATUS_NOT_DEFINED
919984011DefaultI0925NC00000645131/12/2024 09:44GurgaonHaryanaIN12250519984011GurgaonHaryanaIN122505STDADCRDSET159-XLBrocade Co-Ord SetNaNMYNTRAPPMPFalse62045300.04999.03299.03299.0NaN0.04645.541700.0GST120.00353.460.00000.0120.000COMPLETE09AAICN9819MIZLBrocade Co-Ord SetADCRDSET159-XLDELIVEREDSTATUS_NOT_DEFINED
Shipping Address IdCategoryInvoice CodeInvoice CreatedShipping Address CityShipping Address StateShipping Address CountryShipping Address PincodeBilling Address IdBilling Address CityBilling Address StateBilling Address CountryBilling Address PincodeShipping MethodItem SKU CodeItem Type NameItem Type ColorChannel NameGift WrapHSN CodeMRPTotal PriceSelling PriceCost PricePrepaid AmountSubtotalDiscountGST Tax Type CodeCGSTIGSTSGSTUTGSTCESSCGST RateIGST RateSGST RateUTGST RateCESS RateSale Order StatusGSTINSKU NameSeller SKU CodeShipping Courier StatusShipping Tracking Status
585017828753DefaultI0925NC0000037891/10/2024 9:59Gautam Buddha NagarUttar PradeshIN20130117828753Gautam Buddha NagarUttar PradeshIN201301STDADKSET261-MWomen Ethnic Motifs Panelled Sequinned Chanderi Cotton Kurta with Churidar & With DupattaNaNMYNTRAPPMPFalse62045300.06299.04283.04283.0NaN0.05840.122016.0GST12229.440.00229.44006.006.000COMPLETE09AAICN9819MIZLWomen Ethnic Motifs Panelled Sequinned Chanderi Cotton Kurta with Churidar & With DupattaADKSET261-MCOURIER_RETURN-DELIVEREDRTO_DELIVERED_TO_SELLER
585117829195DefaultI0925NC0000037901/10/2024 9:59BengaluruKarnatakaIN56006917829195BengaluruKarnatakaIN560069STDADCRDSET159-XSBrocade Co-Ord SetNaNMYNTRAPPMPFalse62045300.04999.03299.03299.0NaN0.04645.541700.0GST120.00353.460.00000.0120.000COMPLETE09AAICN9819MIZLBrocade Co-Ord SetADCRDSET159-XSCOURIER_RETURN-DELIVEREDRTO_DELIVERED_TO_SELLER
585217831361DefaultI0925NC0000037911/10/2024 9:59Kolkata,West BengalIN70000417831361Kolkata,West BengalIN700004STDADKSET236-LWomen Panelled Kurta with TrousersGreenMYNTRAPPMPFalse62045300.05999.04079.04079.0NaN0.05561.961920.0NaN0.00437.040.00000.0120.000COMPLETE09AAICN9819MIZLWomen Panelled Kurta with TrousersADKSET236-LDELIVEREDSTATUS_NOT_DEFINED
585317831437DefaultI0925NC0000037661/10/2024 9:59Kolkata,West BengalIN70000417831437Kolkata,West BengalIN700004STDADKSET261-XXLWomen Ethnic Motifs Panelled Sequinned Chanderi Cotton Kurta with Churidar & With DupattNaNMYNTRAPPMPFalse62045300.06299.04283.04283.0NaN0.05840.112016.0GST120.00458.890.00000.0120.000COMPLETE09AAICN9819MIZLWomen Ethnic Motifs Panelled Sequinned Chanderi Cotton Kurta with Churidar & With DupattADKSET261-XXLDELIVEREDSTATUS_NOT_DEFINED
585417831642DefaultI0925NC0000037781/10/2024 9:59MumbaiMaharashtraIN40004217831642MumbaiMaharashtraIN400042STDADKSET261-XXLWomen Ethnic Motifs Panelled Sequinned Chanderi Cotton Kurta with Churidar & With DupattNaNMYNTRAPPMPFalse62045300.06299.04283.04283.0NaN0.05840.112016.0GST120.00458.890.00000.0120.000COMPLETE09AAICN9819MIZLWomen Ethnic Motifs Panelled Sequinned Chanderi Cotton Kurta with Churidar & With DupattADKSET261-XXLCOURIER_RETURN-DELIVEREDRTO_DELIVERED_TO_SELLER
585517832698DefaultI0925NC0000037671/10/2024 9:59JallandharPunjabIN14402217832698JallandharPunjabIN144022STDADJMP145-MFloral Printed Culotte JumpsuitNaNMYNTRAPPMPFalse62045300.04599.03127.03127.0NaN0.04263.961472.0GST120.00335.040.00000.0120.000COMPLETE09AAICN9819MIZLFloral Printed Culotte JumpsuitADJMP145-MDELIVEREDSTATUS_NOT_DEFINED
585617834756DefaultI0925NC0000037931/10/2024 9:59KolkataWest BengalIN74314417834756KolkataWest BengalIN743144STDADKSET147-XSWomen Embroidered Regular Thread Work Chanderi Cotton Kurta with Churidar & With DupattaCreamMYNTRAPPMPFalse62045300.06999.03499.03499.0NaN0.06624.113500.0NaN0.00374.890.00000.0120.000COMPLETE09AAICN9819MIZLWomen Embroidered Regular Thread Work Chanderi Cotton Kurta with Churidar & With DupattaADKSET147A-XSDELIVEREDSTATUS_NOT_DEFINED
585717835046DefaultI0925NC0000037691/10/2024 9:59NagpurMaharashtraIN44003317835046NagpurMaharashtraIN440033STDADBLU020-MWoven Design Lapel Collar Jacket and Organza PalazzoNaNMYNTRAPPMPFalse62045300.06499.03639.03639.0NaN0.06109.112860.0GST120.00389.890.00000.0120.000COMPLETE09AAICN9819MIZLWoven Design Lapel Collar Jacket and Organza PalazzoADBLU020-MDELIVEREDSTATUS_NOT_DEFINED
585817835046DefaultI0925NC0000037691/10/2024 9:59NagpurMaharashtraIN44003317835046NagpurMaharashtraIN440033STDADBLU020-XLWoven Design Lapel Collar Jacket and Organza PalazzoNaNMYNTRAPPMPFalse62045300.06499.03639.03639.0NaN0.06109.112860.0GST120.00389.890.00000.0120.000COMPLETE09AAICN9819MIZLWoven Design Lapel Collar Jacket and Organza PalazzoADBLU020-XLDELIVEREDSTATUS_NOT_DEFINED
585917835851DefaultI0925NC0000037831/10/2024 9:59KolkataWest BengalIN70007817835851KolkataWest BengalIN700078STDADKSET240-XSWomen High Slit Pure Silk Kurta with TrousersNaNMYNTRAPPMPFalse62045300.03999.02719.02719.0NaN0.03707.681280.0GST120.00291.320.00000.0120.000COMPLETE09AAICN9819MIZLWomen High Slit Pure Silk Kurta with TrousersADKSET240-XSDELIVEREDSTATUS_NOT_DEFINED

Duplicate rows

Most frequently occurring

Shipping Address IdCategoryInvoice CodeInvoice CreatedShipping Address CityShipping Address StateShipping Address CountryShipping Address PincodeBilling Address IdBilling Address CityBilling Address StateBilling Address CountryBilling Address PincodeShipping MethodItem SKU CodeItem Type NameItem Type ColorChannel NameGift WrapHSN CodeMRPTotal PriceSelling PricePrepaid AmountSubtotalDiscountGST Tax Type CodeCGSTIGSTSGSTUTGSTCESSCGST RateIGST RateSGST RateUTGST RateCESS RateSale Order StatusGSTINSKU NameSeller SKU CodeShipping Courier StatusShipping Tracking Status# duplicates
017147660DefaultI0925NC00000322528/08/2024 09:54JamshedpurJharkhandIN83211017147660JamshedpurJharkhandIN832110STDADKSET116-2XLEthnic Motifs Regular Chanderi Silk Kurta With Trousers & DupattaNaNMYNTRAPPMPFalse62045300.04999.02899.02899.00.04688.3952100.0GST120.000310.6050.000000.0120.000COMPLETE09AAICN9819MIZLEthnic Motifs Regular Chanderi Silk Kurta With Trousers & DupattaADKSET116-2XLDELIVEREDSTATUS_NOT_DEFINED2
117277998DefaultI0925NC0000033429/4/2024 10:01BhavnagarGujaratIN36400217277998BhavnagarGujaratIN364002STDADJMP145-XSFloral Printed Culotte JumpsuitNaNMYNTRAPPMPFalse62045300.04599.03219.03219.00.04254.1051380.0GST120.000344.8950.000000.0120.000COMPLETE09AAICN9819MIZLFloral Printed Culotte JumpsuitADJMP145-XSDELIVEREDSTATUS_NOT_DEFINED2
218259525DefaultI0925NC00000433317/10/2024 10:01New DelhiDelhiIN11004318259525New DelhiDelhiIN110043STDADCRDSET188-SPrinted Tunic With TrousersNaNMYNTRAPPMPFalse62045300.02999.01979.01979.00.02786.9651020.0GST120.000212.0350.000000.0120.000COMPLETE09AAICN9819MIZLPrinted Tunic With TrousersADCRDSET188-SCOURIER_RETURN-DELIVEREDRTO_DELIVERED_TO_SELLER2
318398525DefaultI0925NC00000460322/10/2024 17:36RampurUttar PradeshIN24490118398525RampurUttar PradeshIN244901STDADKSET161A-LWomen Embroidered High Slit Sequinned Silk Georgette Kurta with Sharara & With DupattaRedMYNTRAPPMPFalse62045300.0NaN3709.03709.00.06601.6003290.0NaN198.7000.000198.700006.006.000COMPLETE09AAICN9819MIZLWomen Embroidered High Slit Sequinned Silk Georgette Kurta with Sharara & With DupattaADKSET161A-LDELIVEREDSTATUS_NOT_DEFINED2
418530461DefaultI0925NC00000489926/10/2024 17:15LucknowUttar PradeshIN22602218530461LucknowUttar PradeshIN226022STDADCRDSET263-MPrinted Tunic With PalazzzosNaNMYNTRAPPMPFalse62045300.05999.04439.04439.00.05523.4001560.0GST12237.8000.000237.800006.006.000COMPLETE09AAICN9819MIZLPrinted Tunic With PalazzzosADCRDSET263-MDELIVEREDSTATUS_NOT_DEFINED2
518634020DefaultI0925NC00000501530/10/2024 16:53DelhiDelhiIN11009318634020DelhiDelhiIN110093STDADKSET116-XLEthnic Motifs Regular Chanderi Silk Kurta With Trousers & DupattaNaNMYNTRAPPMPFalse62045300.04999.02699.02699.00.04709.8202300.0GST120.000289.1800.000000.0120.000COMPLETE09AAICN9819MIZLEthnic Motifs Regular Chanderi Silk Kurta With Trousers & DupattaADKSET116-XLCOURIER_RETURN-DELIVEREDRTO_DELIVERED_TO_SELLER2
618775768DefaultI0925NC0000051987/11/2024 9:51BhagalpurBiharIN81200218775768BhagalpurBiharIN812002STDADKSET161A-XSWomen Embroidered High Slit Sequinned Silk Georgette Kurta with Sharara & With DupattaRedMYNTRAPPMPFalse62045300.0NaN3709.03709.00.06601.6053290.0NaN0.000397.3950.000000.0120.000COMPLETE09AAICN9819MIZLWomen Embroidered High Slit Sequinned Silk Georgette Kurta with Sharara & With DupattaADKSET161A-XSCOURIER_RETURN-DELIVEREDRTO_DELIVERED_TO_SELLER2
719384236DefaultI0925NC0000059343/12/2024 10:21NawadaBiharIN80513119384236NawadaBiharIN805131STDADKSET148-MWoven Design Embroidered Thread Work Anarkali Kurta with Churidar & DupattaBurgandyMYNTRAPPMPFalse62045300.06599.04487.04487.00.06118.2502112.0NaN0.000480.7500.000000.0120.000COMPLETE09AAICN9819MIZLWoven Design Embroidered Thread Work Anarkali Kurta with Churidar & DupattaADKSET148-MDELIVEREDSTATUS_NOT_DEFINED2
820312124DefaultI0925NC00000679818/01/2025 09:48LucknowUttar PradeshIN22600420312124LucknowUttar PradeshIN226004STDADKSET148-LWoven Design Embroidered Thread Work Anarkali Kurta with Churidar & DupattaBurgandyMYNTRAPPMPFalse62045300.06599.04487.04487.00.06118.2502112.0GST12240.3750.000240.375006.006.000COMPLETE09AAICN9819MIZLWoven Design Embroidered Thread Work Anarkali Kurta with Churidar & DupattaADKSET148-LCOURIER_RETURN-DELIVEREDRTO_DELIVERED_TO_SELLER2
920900823DefaultI0925NC00000769321/02/2025 10:02BhopalMadhya PradeshIN46200120900823BhopalMadhya PradeshIN462001STDADKSET148-MWoven Design Embroidered Thread Work Anarkali Kurta with Churidar & DupattaBurgandyMYNTRAPPMPFalse62045300.06599.04487.04487.00.06118.2502112.0NaN0.000480.7500.000000.0120.000COMPLETE09AAICN9819MIZLWoven Design Embroidered Thread Work Anarkali Kurta with Churidar & DupattaADKSET148-MSHIPPEDSTATUS_NOT_DEFINED2